JP2019039685A - Inspection system and inspection method - Google Patents

Abstract

To obtain data (or data equivalent to the data) obtained by phase-only Laplacian processing at higher speed.SOLUTION: An inspection system according to an embodiment comprises: a planar illumination unit which causes a periodical time variation and space variation of light intensity; an image data acquisition unit which acquires complex time correlation image data from a time correlation camera or an imaging system performing operation equivalent to that of the camera; and an arithmetic processing unit which detects an abnormality on the basis of Laplacian image data obtained by applying Laplacian processing and residue arithmetic processing to phase image data extracted from the complex time correlation image data.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to an inspection system and an inspection method.

  Conventionally, a time that includes not only the intensity of light but also information about temporal transition of light by irradiating the object to be inspected periodically and imaging the reflected light from the surface of the object to be inspected Techniques for acquiring correlated image data have been proposed. Such time-correlated image data is used, for example, to detect abnormality of the object to be inspected.

Japanese Patent Laid-Open No. 2015-200362

  The time correlation image data can be acquired as complex data including an amplitude component and a phase component. When detecting anomalies from time-correlated image data as complex number data, phase-only Laplacian processing is performed to ignore the amplitude component and perform second-order differentiation only on the phase component in order to detect local changes in the phase component May be.

  However, since complex data is generally handled as two types of data divided into real and imaginary parts, phase-only Laplacian processing for complex data requires complicated calculations and is completed. It takes a long time to complete.

  Therefore, a new calculation method that can obtain data (and equivalent data) obtained by phase-only Laplacian processing at higher speed is desired.

  The inspection system according to the embodiment includes a planar illumination unit that gives periodic temporal and spatial changes in light intensity, and an image that acquires complex time correlation image data from a time correlation camera or an imaging system that performs an equivalent operation. A data acquisition unit; and an arithmetic processing unit that detects an abnormality based on Laplacian image data obtained by performing Laplacian processing and remainder calculation processing on phase image data extracted from complex-time correlation image data.

Drawing 1 is a figure showing the example of composition of the inspection system of an embodiment. FIG. 2 is a block diagram illustrating a configuration of the time correlation camera of the embodiment. FIG. 3 is a conceptual diagram showing frames accumulated in time series by the time correlation camera of the embodiment. FIG. 4 is a diagram illustrating an example of a fringe pattern irradiated by the illumination device of the embodiment. FIG. 5 is a diagram illustrating a first detection example of abnormality of the inspection object by the time correlation camera of the embodiment. FIG. 6 is a diagram illustrating an example of the amplitude of light that changes in accordance with the abnormality when the abnormality illustrated in FIG. 5 is present in the test object in the embodiment. FIG. 7 is a diagram illustrating a second detection example of an abnormality of an object to be inspected by the time correlation camera of the embodiment. FIG. 8 is a diagram illustrating a third example of detection of abnormality of an object to be inspected by the time correlation camera of the embodiment. FIG. 9 is a diagram illustrating an example of a fringe pattern output from the illumination control unit of the embodiment to the illumination device. FIG. 10 is a diagram illustrating an example of a wave shape representing a stripe pattern after passing through the screen of the embodiment. FIG. 11 is a diagram illustrating an example of a phase image obtained by the inspection system of the embodiment. FIG. 12 is a schematic diagram showing changes in phase components in complex time correlation image data corresponding to the phase image shown in FIG. FIG. 13 is a diagram illustrating an example of a Laplacian filter that can be used in the embodiment. FIG. 14 is a diagram illustrating an example of a Laplacian image obtained by imaging Laplacian image data obtained by performing phase-only Laplacian processing on complex-time correlation image data corresponding to the phase image of FIG. 11. FIG. 15 is a flowchart showing a series of processing executed when the inspection system of the embodiment inspects an object to be inspected. FIG. 16 is a flowchart showing details of image processing executed by the inspection system according to the embodiment. FIG. 17 is a diagram illustrating an example of the phase image used in the experiment for confirming the accuracy of the result obtained by the image processing according to the embodiment. FIG. 18 is a diagram illustrating an example of a Laplacian image obtained by imaging the result of performing the phase-only Laplacian process on the complex time correlation image data corresponding to the phase image of FIG. FIG. 19 is a diagram showing an example of a Laplacian image obtained by imaging the result of performing the Laplacian process and the remainder calculation process on the phase image data corresponding to the phase image of FIG. FIG. 20 is a diagram showing the degree of coincidence between the Laplacian image of FIG. 18 and the Laplacian image of FIG. FIG. 21 is a diagram illustrating a switching example of the fringe pattern output by the illumination control unit of the second modified example. FIG. 22 is a diagram illustrating an example in which the illumination control unit according to the second modification irradiates the surface of the object to be inspected including an abnormality (defect) with a stripe pattern. FIG. 23 is a diagram showing the relationship between an abnormality (defect) and a stripe pattern on the screen when the stripe pattern is changed in the y direction in the second modification. FIG. 24 is a diagram illustrating an example of a fringe pattern output from the illumination control unit of the third modified example to the illumination device.

<Embodiment>
Hereinafter, the inspection system of the embodiment will be described. The inspection system of the embodiment has various configurations for inspecting an object to be inspected. Drawing 1 is a figure showing the example of composition of the inspection system of an embodiment. As shown in FIG. 1, the inspection system according to the embodiment includes a PC 100, a time correlation camera 110, a lighting device 120, a screen 130, and an arm 140.

  The arm 140 is used to fix the inspection object 150 and changes the position and orientation of the surface of the inspection object 150 that can be imaged by the time correlation camera 110 according to control from the PC 100.

  The illuminating device 120 is a device that irradiates light to the object 150 to be inspected, and can control the intensity of irradiated light in units of regions in accordance with a stripe pattern from the PC 100. Furthermore, the illuminating device 120 can control the intensity | strength of the light of the said area unit according to periodic time transition. In other words, the lighting device 120 can give a periodic temporal change and a spatial change of the light intensity. A specific light intensity control method will be described later.

  The screen 130 diffuses the light output from the illuminating device 120 and then irradiates the test object 150 with light in a plane. The screen 130 according to the embodiment irradiates the object 150 in a surface with the light input from the lighting device 120 and subjected to periodic time change and space change. An optical system component (not shown) such as a condensing Fresnel lens may be provided between the illumination device 120 and the screen 130.

  In addition, although embodiment demonstrates the example which comprises the planar illumination part which combines the illumination device 120 and the screen 130 and gives the periodic time change and spatial change of light intensity, the illumination part of embodiment is described. It is not limited to such a combination. In the embodiment, for example, the illumination unit may be configured by arranging LEDs on a surface or arranging a large monitor.

  FIG. 2 is a block diagram illustrating a configuration of the time correlation camera 110 according to the embodiment. The time correlation camera 110 includes an optical system 210, an image sensor 220, a data buffer 230, a control unit 240, and a reference signal output unit 250.

  The optical system 210 includes an imaging lens and the like, transmits a light beam from a subject (including the inspection object 150) outside the time correlation camera 110, and forms an optical image of the subject formed by the light beam.

  The image sensor 220 is a sensor that can output the intensity of light incident through the optical system 210 as a light intensity signal at high speed for each pixel.

  The light intensity signal of the embodiment is a signal obtained by the illumination device 120 of the inspection system irradiating a subject (including the inspected object 150) with light and the image sensor 220 receiving reflected light from the subject.

  The image sensor 220 is, for example, a sensor that can be read out at a higher speed than a conventional sensor, and has a two-dimensional planar shape in which pixels are arranged in two kinds of directions: a row direction (x direction) and a column direction (y direction). It shall be configured. Each pixel of the image sensor 220 is defined as a pixel P (1,1),..., P (i, j),..., P (X, Y) (Note that the image size of the embodiment is XX). Y). Note that the reading speed of the image sensor 220 is not limited and may be the same as the conventional one.

  The image sensor 220 receives a light beam from the subject (including the inspected object 150) transmitted by the optical system 210 and photoelectrically converts the light intensity signal (intensity of light reflected from the subject) ( A two-dimensional planar frame composed of (imaging signals) is generated and output to the control unit 240. The image sensor 220 according to the embodiment outputs the frame for each readable unit time.

  The control unit 240 according to the embodiment includes hardware similar to that of a normal computer such as a processor and a memory, and the software (inspection program) stored in the memory is executed by the processor, so that the transfer unit 241 and the reading unit 242, intensity image superimposing unit 243, first multiplier 244, first correlation image superimposing unit 245, second multiplier 246, second correlation image superimposing unit 247, image An output unit 248. In the embodiment, part or all of the functional configuration of the control unit 240 may be realized only by dedicated hardware (circuit) using an FPGA, an ASIC, or the like.

  The transfer unit 241 stores the frames composed of the light intensity signals output from the image sensor 220 in the data buffer 230 in time series order.

  The data buffer 230 accumulates frames composed of light intensity signals output from the image sensor 220 in time series.

  FIG. 3 is a conceptual diagram illustrating frames accumulated in time series in the time correlation camera 110 according to the embodiment. As shown in FIG. 3, the data buffer 230 of the embodiment stores a plurality of light intensity signals G (1, 1, t),... At each time t (t = t0, t1, t2,..., Tn). .., G (i, j, t),..., G (X, Y, t), a plurality of frames Fk (k = 1, 2,..., N) are accumulated in chronological order. Is done. Note that one frame generated at time t is a light intensity signal G (1, 1, t),..., G (i, j, t), ..., G (X, Y, t). Composed.

  The light intensity signal (imaging signal) G (1,1, t),..., G (i, j, t),..., G (X, Y, t) of the embodiment has a frame Fk (k = Each pixel P (1, 1),..., P (i, j),.

  The frame output from the image sensor 220 includes only a light intensity signal, in other words, it can be considered as monochrome image data. In the embodiment, an example in which the image sensor 220 generates monochrome image data in consideration of resolution, sensitivity, cost, and the like will be described. However, the image sensor 220 is not limited to a monochrome image sensor. Alternatively, a color image sensor may be used.

  Returning to FIG. 2, the reading unit 242 of the embodiment receives the light intensity signals G (1,1, t),..., G (i, j, t),. , T) are read out in frame-by-frame order and output to the first multiplier 244, the second multiplier 246, and the intensity image superimposing unit 243.

  The time correlation camera 110 according to the embodiment generates image data for each output destination of the reading unit 242. In other words, the temporal phase camera 110 generates three types of image data.

  The time correlation camera 110 according to the embodiment generates intensity image data and two types of time correlation image data as three types of image data. The embodiment is not limited to generating the intensity image data and the two types of time correlation image data. When the intensity image data is not generated, one type or three or more types of time correlations are used. A case where image data is generated is also conceivable.

  As described above, the image sensor 220 according to the embodiment outputs a frame composed of a light intensity signal for each unit time. However, in order to generate normal image data, a light intensity signal corresponding to the exposure time necessary for imaging is required. Therefore, in the embodiment, the intensity image superimposing unit 243 generates intensity image data by superimposing a plurality of frames for an exposure time necessary for imaging. In addition, each pixel value (value representing the intensity of light) G (x, y) of the intensity image data can be derived from the following equation (1). The exposure time is the time difference between t0 and tn.

  Thereby, the intensity image data in which the subject (including the inspected object 150) is imaged is generated in the same manner as the conventional camera imaging. Then, the intensity image superimposing unit 243 outputs the generated intensity image data to the image output unit 248.

  The time correlation image data is image data indicating changes in light intensity according to time transition. That is, in the embodiment, for each frame in time series order, the light intensity signal included in the frame is multiplied by the reference signal indicating the time transition, and the time that is the result of multiplying the reference signal and the light intensity signal is obtained. Temporal correlation image data is generated by generating a temporal correlation value frame composed of correlation values and superimposing a plurality of temporal correlation value frames.

  By the way, in order to detect an abnormality of the inspection object 150 using the time correlation image data, it is necessary to change the light intensity signal input to the image sensor 220 in synchronization with the reference signal. For this reason, as described above, the illumination device 120 according to the embodiment is configured to irradiate the surface light through the screen 130 so as to periodically change the time and spatially move the stripes. The

In the embodiment, two types of time correlation image data are generated. The reference signal may be a signal representing a time transition, but in the embodiment, a complex sine wave e ^−jωt is used. It is assumed that the angular frequency is ω and the time is t. The angular frequency so that the complex sine wave e ^−jωt representing the reference signal correlates with one period of the above-described exposure time (in other words, the time necessary for generating the intensity image data and the time correlation image data). Assume that ω is set. In other words, the planar and dynamic light formed by the illumination unit 120 and the illumination unit such as the screen 130 has a first period (time period) at each position on the surface (reflection surface) of the object 150 to be inspected. And a distribution of increase or decrease in spatial irradiation intensity in a second period (spatial period) along at least one direction along the surface. When this planar light is reflected by the surface, it is complex-modulated according to the specifications of the surface (normal vector distribution, etc.). The time correlation camera 110 receives the light complex-modulated on the surface and performs quadrature detection (orthogonal demodulation) using the reference signal of the first period, thereby obtaining time correlation image data as a complex signal. By modulation / demodulation based on such time-correlated image data as complex signals, it is possible to detect features corresponding to the distribution of normal vectors on the surface.

The complex sine wave e ^−jωt can also be expressed as e ^−jωt = cos ωt−j · sin ωt. Accordingly, each pixel value C (x, y) of the time correlation image data can be derived from the following equation (2).

  In the embodiment, in the formula (2), two types of time correlation image data are divided into a pixel value C1 (x, y) representing the real part and a pixel value C2 (x, y) representing the imaginary part. Generate.

For this reason, the reference signal output unit 250 generates and outputs different reference signals for the first multiplier 244 and the second multiplier 246, respectively. Embodiment of the reference signal output section 250 outputs the first reference signal cosωt corresponding to the real part of the complex sine wave e ^-Jeiomegati the first multiplier 244, corresponding to the imaginary part of the complex sine wave e ^-Jeiomegati The second reference signal sin ωt is output to the second multiplier 246. As described above, the reference signal output unit 250 according to the embodiment outputs two types of reference signals expressed as time functions of a sine wave and a cosine wave that form a Hilbert transform pair as an example. However, the reference signal is not limited to the example described here, and may be a reference signal that changes according to a time transition, such as a time function.

Then, the first multiplier 244 ^calculates the real part cosωt of the complex sine wave e ^−jωt input from the reference signal output unit 250 for each light intensity signal of the frame in units of frames input from the reading unit 242. Multiply.

  The first correlation image superimposing unit 245 performs a process of superimposing the multiplication result of the first multiplier 244 on a pixel-by-pixel basis for a plurality of frames for an exposure time necessary for imaging. Thereby, each pixel value C1 (x, y) of the first time correlation image data is derived from the following equation (3).

Then, the second multiplier 246 multiplies the light intensity signal of the frame input from the reading unit 242 by the imaginary part sinωt of the complex sine wave e ^−jωt input from the reference signal output unit 250.

  The second correlation image superimposing unit 247 performs a process of superimposing the multiplication result of the second multiplier 246 on a pixel-by-pixel basis for a plurality of frames corresponding to the exposure time necessary for imaging. Thereby, each pixel value C2 (x, y) of the second time correlation image data is derived from the following equation (4).

  By performing the processing described above, two types of time correlation image data, in other words, time correlation image data having two degrees of freedom can be generated.

The embodiment does not limit the type of reference signal. For example, in the embodiment, two types of time correlation image data of the real part and the imaginary part of the complex sine wave e ^−jωt are generated, but two types of image data based on the light amplitude and the light phase are generated. May be.

  In addition, the time correlation camera 110 according to the embodiment can generate a plurality of systems as time correlation image data. Thereby, for example, when light in which stripes having a plurality of widths are combined is irradiated, two types of time correlation image data of the real part and the imaginary part described above can be generated for each stripe width. For this purpose, the time correlation camera 110 includes a combination of two multipliers and two correlation image superimposing units for a plurality of systems, and the reference signal output unit 250 has an angular frequency suitable for each system. The reference signal by ω can be output.

  Then, the image output unit 248 outputs two types of time correlation image data and intensity image data to the PC 100. As a result, the PC 100 detects an abnormality of the inspected object 150 using the two types of time correlation image data and the intensity image data. For that purpose, it is necessary to irradiate the test object 150 with light.

  The illuminating device 120 of embodiment irradiates the fringe pattern which moves at high speed. FIG. 4 is a diagram illustrating an example of a fringe pattern irradiated by the illumination device 120 of the embodiment. The example illustrated in FIG. 4 is an example in which the stripe pattern is scrolled (moved) in the x direction. A white area is a bright area corresponding to the stripe, and a black area is an interval area (dark area) corresponding to the stripe.

  In the embodiment, the fringe pattern irradiated by the illuminating device 120 moves by one period with the exposure time when the time correlation camera 110 captures the intensity image data and the time correlation image data. Thereby, the illuminating device 120 gives the time change of the light intensity periodically by the spatial movement of the stripe pattern of the light intensity. In the embodiment, by associating the time during which the fringe pattern in FIG. 4 moves by one period with the exposure time, each pixel of the time-correlated image data has information on the light intensity signal for at least one period of the fringe pattern. Is embedded.

  As shown in FIG. 4, in the embodiment, an example in which the illumination device 120 irradiates a stripe pattern based on a rectangular wave will be described, but other than a rectangular wave may be used. In the embodiment, the illumination device 120 is irradiated through the screen 130, so that the boundary area between the light and dark of the rectangular wave can be blurred.

  In the embodiment, the fringe pattern irradiated by the illumination device 120 is represented as A (1 + cos (ωt + kx)). That is, the stripe pattern includes a plurality of stripes repeatedly (periodically). Note that the intensity of light applied to the inspection object 150 can be adjusted between 0 and 2A, and is a light phase kx. k is the wave number of the stripe. x is the direction in which the phase changes.

  Then, the fundamental frequency component of the light intensity signal f (x, y, t) of each pixel of the frame can be expressed as the following equation (5). As shown in Expression (5), the brightness of the stripe changes in the x direction.

f (x, y, t) = A (1 + cos (ωt + kx))
= A + A / 2 {e ^{j (ωt + kx)} + e ^{−j (ωt + kx)} } (5)

  As shown in Expression (5), the intensity signal of the fringe pattern irradiated by the illumination device 120 can be considered as a complex number.

  Then, the light from the illumination device 120 is reflected and input to the image sensor 220 from the subject (including the inspection object 150).

  Therefore, the light intensity signal G (x, y, t) input to the image sensor 220 is used as the light intensity signal f (x, y, t) of each pixel of the frame when the illumination device 120 is irradiated. Can do. Therefore, by substituting equation (5) into equation (1) for deriving intensity image data, equation (6) shown below can be derived. Note that the phase is kx.

  From Expression (6), it can be confirmed that each pixel of the intensity image data is input with a value obtained by multiplying the exposure time T by the intermediate value A of the intensity of the light output from the illumination device 120. Further, by substituting equation (5) into equation (2) for deriving time correlation image data, equation (7) shown below can be derived. Note that AT / 2 is the amplitude and kx is the phase.

  As a result, the complex number of time correlation image data (which may be referred to as complex time correlation image data) represented by Equation (7) can be replaced with the two types of time correlation image data described above. That is, the time correlation image data composed of the real part and the imaginary part described above includes a phase change and an amplitude change in the light intensity change irradiated on the object 150. In other words, the PC 100 according to the embodiment can detect the phase change of the light emitted from the illumination device 120 and the light amplitude change based on the two types of time correlation image data.

  Therefore, the PC 100 according to the embodiment includes a phase image representing a phase change of light entering each pixel and an amplitude image representing the amplitude of light entering each pixel based on the time correlation image data and the intensity image data. Generate. In addition, the PC 100 generates an intensity image representing the intensity of light entering each pixel based on the intensity image data. Then, the PC 100 detects an abnormality of the inspected object 150 based on at least one of the phase image, the amplitude image, and the intensity image.

  By the way, when an abnormality based on the unevenness occurs in the surface shape of the inspection object 150, the distribution of the normal vectors on the surface of the inspection object 150 changes corresponding to the abnormality. In addition, when an abnormality that absorbs light occurs on the surface of the inspection object 150, the intensity of the reflected light changes. A change in the normal vector distribution is detected as at least one of a phase change and an amplitude change of light. Therefore, in the embodiment, using the time correlation image data, at least one of the light phase change and the amplitude change corresponding to the change in the normal vector distribution is detected. As a result, it is possible to specify a region where there is a possibility that a surface shape abnormality exists. Hereinafter, the relationship between the abnormality of the inspected object 150, the normal vector, and the light phase change or amplitude change will be described by way of example.

  FIG. 5 is a diagram illustrating a first detection example of abnormality of an object to be inspected by the time correlation camera 110 according to the embodiment. In the example shown in FIG. 5, it is assumed that the inspected object 500 has a projecting shape abnormality 501. In this situation, it can be confirmed that the normal vectors 521, 522, and 523 are in different directions in the region near the point 502 of the abnormality 501. Then, the normal vectors 521, 522, 523 are directed in different directions, so that diffusion (for example, light 511, 512, 513) occurs in the light reflected from the anomaly 501, and the image sensor 220 of the time correlation camera 110. The width 503 of the fringe pattern entering the arbitrary pixel 531 is increased.

  FIG. 6 is a diagram showing an example of the amplitude of light that changes in accordance with the abnormality 501 shown in FIG. In the example shown in FIG. 6, the amplitude of light is divided into a real part (Re) and an imaginary part (Im) and is represented on a two-dimensional plane. In FIG. 6, the light amplitudes 611, 612, and 613 corresponding to the lights 511, 512, and 513 in FIG. The light amplitudes 611, 612, and 613 cancel each other, and light having an amplitude 621 is incident on the arbitrary pixel 531 of the image sensor 220.

  Therefore, in the situation shown in FIG. 6, it can be confirmed that the amplitude is locally small in the region where the abnormality 501 of the inspection object 500 is imaged. In other words, when there is a region that is darker than the surroundings in the amplitude image showing the amplitude change, it can be assumed that there is local cancellation of the amplitude of the light in the region. It can be determined that an abnormality 501 has occurred at the position of the inspection object 500 corresponding to the region. Here, the case where the amplitude image is locally dark in the region corresponding to the protrusion-shaped abnormality 501 is illustrated, but the amplitude image is also locally dark in the region corresponding to the dent-like abnormality such as a scratch. Become.

  The inspection system of the embodiment is not limited to the one in which the inclination changes steeply like the abnormality 501 in FIG. 5, and can also detect an abnormality that changes gently. FIG. 7 is a diagram illustrating a second detection example of the abnormality of the inspected object by the time correlation camera 110 according to the embodiment. FIG. 7 shows a situation in which a gentle gradient 701 is generated in the inspected object 700 having a normal surface that is planar (in other words, the normal lines are parallel). In such a situation, the normal vectors 721, 722, and 723 on the gradient 701 also change gently. Accordingly, the light beams 711, 712, and 713 input to the image sensor 220 are also shifted little by little. In the example shown in FIG. 7, since the light amplitudes do not cancel each other due to the gentle gradient 701, the light amplitudes shown in FIGS. 5 and 6 hardly change. However, although the light originally projected from the screen 130 should enter the image sensor 220 as it is, the light projected from the screen 130 does not enter the image sensor 220 in a parallel state due to the gentle gradient 701. In addition, a phase change occurs in the light. Accordingly, by detecting the difference between the light phase change and the surroundings, an abnormality due to the gentle gradient 701 as shown in FIG. 7 can be detected.

  In addition, there may be an abnormality other than the surface shape of the inspection object (in other words, the distribution of the normal vector of the inspection object). FIG. 8 is a diagram illustrating a third example of detection of abnormality of an object to be inspected by the time correlation camera 110 according to the embodiment. In the example shown in FIG. 8, dirt 801 is attached to the object 800 to be inspected, so that the light irradiated from the illumination device 120 is absorbed or diffusely reflected, and the dirt 801 of the time correlation camera 110 is imaged. This represents an example in which the light intensity hardly changes in an arbitrary pixel region. In other words, in an arbitrary pixel area where the dirt 801 is imaged, changes in the light intensity cancel each other and cancel each other, resulting in almost direct current brightness.

  In such a case, in the pixel region where the dirt 801 is imaged, there is almost no light amplitude. Therefore, when an amplitude image is displayed, a region darker than the surroundings is generated. Therefore, it can be estimated that the dirt 801 is present at the position of the inspection object 800 corresponding to the region.

  As described above, in the embodiment, the abnormality in the inspected object is specified by detecting the change in amplitude in the change in light intensity and the change in phase in the change in light intensity based on the time correlation image data. be able to.

  Returning to FIG. 1, the PC 100 will be described. The PC 100 controls the entire detection system. The PC 100 includes an arm control unit 101, an illumination control unit 102, and a control unit 103.

  The arm control unit 101 controls the arm 140 in order to change the surface of the object 150 to be imaged by the time correlation camera 110. In the embodiment, a plurality of surfaces to be imaged of the inspection object 150 are set in the PC 100 in advance. Then, every time the time correlation camera 110 completes imaging of the test object 150, the arm control unit 101 controls the arm 140 according to the setting so that the surface on which the time correlation camera 110 is set can be imaged. The inspection object 150 is moved. Note that the moving method of the arm 140 according to the embodiment is not limited to repeating the movement of the arm 140 every time imaging is completed and stopping the imaging before the imaging is started, and the arm 140 is continuously moved. Driving can also be included. The arm 140 may also be referred to as a transport unit, a moving unit, a position changing unit, a posture changing unit, or the like.

  The illumination control unit 102 outputs a fringe pattern irradiated by the illumination device 120 in order to inspect the inspected object 150. The illumination control unit 102 according to the embodiment transfers at least three or more stripe patterns to the illumination device 120 and instructs the illumination device 120 to switch and display the stripe patterns during the exposure time.

  FIG. 9 is a diagram illustrating an example of a fringe pattern output from the illumination control unit 102 to the illumination device 120. In accordance with the rectangular wave shown in FIG. 9B, the illumination control unit 102 performs control so that the stripe pattern in which the black region and the white region shown in FIG. 9A are set is output.

  The stripe interval (stripe width) for each stripe pattern irradiated in the embodiment is set according to the size of the abnormality (defect) to be detected, and detailed description thereof is omitted here.

  In the embodiment, it is assumed that the angular frequency ω of the rectangular wave for outputting the fringe pattern matches the angular frequency ω of the reference signal.

  As shown in FIG. 9, the fringe pattern output from the illumination control unit 102 can be shown as a rectangular wave, but the border area of the fringe pattern is blurred through the screen 130, that is, the bright area in the fringe pattern. By making the intensity change of light at the boundary between the (stripe region) and the dark region (interval region) gentle (dull), it can be approximated to a sine wave. FIG. 10 is a diagram illustrating an example of a wave shape representing a stripe pattern after passing through the screen 130. As shown in FIG. 10, the measurement accuracy can be improved by the wave shape approaching a sine wave. Further, a gray region in which the brightness changes in multiple steps may be added to the stripe, or a gradation may be given. Further, a stripe pattern including color stripes may be used.

  Returning to FIG. 1, the control unit 103 includes an image data acquisition unit 104 and an arithmetic processing unit 105. Similar to the control unit 240 described above, in the embodiment, each functional module included in the control unit 103 may be realized by cooperation of hardware and software, or may be realized only by hardware.

  The image data acquisition unit 104 acquires intensity image data and time correlation image data (complex time correlation image data) from the time correlation camera 110. According to these image data, complex image data representing both the amplitude and phase in the intensity change of light entering every pixel, amplitude image data representing the amplitude in the intensity change of light entering every pixel, and entering each pixel. It is possible to calculate (generate, extract) phase image data representing the phase of light intensity change, intensity image data representing the intensity of light entering each pixel, and the like.

  In the embodiment, the image data acquisition unit 104 does not use time-correlated image data (complex time-correlated image data) corresponding to polar complex numbers divided by amplitude components and phase components, but uses the complex numbers as real parts and imaginary parts. It is assumed that two types of data divided by the section are received from the time correlation camera 110.

  The embodiment does not limit the calculation method of the amplitude image data. For example, the pixel values C1 (x, y) and C2 (x, y) of two types of time correlation image data acquired as complex time correlation image data are used. From y), it is possible to derive each pixel value F (x, y) of the amplitude image by using the following equation (8).

  Further, from the pixel values C1 (x, y) and C2 (x, y) of the two types of time correlation image data acquired as complex time correlation image data, a phase image is obtained using the following equation (9). It is possible to derive each pixel value P (x, y) of the data.

  In addition, since it cannot be overemphasized that intensity | strength image data can be produced | generated based on intensity | strength image data, description about the derivation | leading-out method of each pixel value of intensity | strength image data is abbreviate | omitted here.

  The arithmetic processing unit 105 includes an image processing unit 106 and an abnormality detection processing unit 107. The image processing unit 106 performs predetermined image processing on various image data acquired by the image data acquisition unit 104. The abnormality detection processing unit 107 detects an abnormality of the inspected object 150 based on the image after the image processing by the image processing unit 106 is performed.

  Examples of image processing that can be executed for abnormality detection using complex-time correlation image data include, for example, phase-only Laplacian processing. As will be described below, the phase-only Laplacian process is a process for ignoring the amplitude component in the complex-time correlation image data expressed by complex numbers including the amplitude and the phase and performing the second-order differentiation only on the phase component. This is a process of canceling the steady change of the component and extracting only the steep change of the phase component.

  Hereinafter, the phase-only Laplacian process will be described more specifically.

  As can be seen from Equation (9), each pixel value of the phase image data is folded in the range of −π to π. Accordingly, each pixel value of the phase image data can be periodically and discontinuously changed from −π to π or from π to −π (phase jump). Thereby, even when the inspection target surface is configured with a flat surface that does not include local abnormalities (defects) such as unevenness, the phase image data obtained by imaging the flat surface is imaged on the phase image. Periodic edges appear due to the effects of phase jumps.

  FIG. 11 is a diagram illustrating an example of a phase image obtained by the inspection system of the embodiment. A phase image 1100 shown in FIG. 11 includes a plurality of edge portions 1101 that appear periodically due to the influence of the phase jump and a local abnormal portion 1102 that is an original detection target.

  In the phase image 1100 shown in FIG. 11, the change in the pixel value in the abnormal portion 1102 is smaller than the change in the pixel value in the edge portion 1101. Therefore, the edge portion 1101 becomes noise and it is not easy to detect the abnormal portion 1102 simply by performing normal detection processing using a threshold value or the like on the phase image shown in FIG.

  On the other hand, FIG. 12 is a schematic diagram showing changes in phase components in complex time correlation image data corresponding to the phase image 1100 shown in FIG. As shown in FIG. 12, the phase component changes sharply in the local abnormal portion 1102, but the phase component changes smoothly (linearly) in a flat portion other than the abnormal portion 1102. . Therefore, if the change in the stationary phase component in the flat portion can be ignored, only the sharp change in the phase component in the local abnormal portion 1102 can be easily detected, which is beneficial.

  Therefore, in the embodiment, Laplacian image data is acquired by performing phase-only Laplacian processing using a Laplacian filter as described below on complex time correlation image data obtained by the time correlation camera 110, and the Laplacian image data is obtained. Is subjected to threshold processing, thereby detecting an abnormality as a portion where the phase component changes locally.

  FIG. 13 is a diagram illustrating an example of a Laplacian filter that can be used in the embodiment. The Laplacian filter 1300 in FIG. 13 calculates the difference between the pixel value of a pixel to be processed and the pixel values of eight pixels adjacent to the periphery of the pixel to be processed in image data having a normal real pixel value. This is a so-called 8-neighbor Laplacian filter.

  More specifically, the Laplacian filter 1300 in FIG. 13 includes eight adjacent pixels around the pixel to be processed from a value obtained by multiplying the pixel value of the pixel to be processed by eight times in image data having a normal real pixel value. This is a filter for setting a value obtained by subtracting the sum of the pixel values of the pixels to be a new pixel value of the pixel to be processed.

Here, as described above, each pixel value of the complex time correlation image data obtained by the time correlation camera 110 is expressed by a complex exponential function including an amplitude component and a phase component, and the phase component is an exponent part of the complex exponential function. include. Therefore, when the processing corresponding to the Laplacian filter 1300 in FIG. 13 is to be performed only on the phase component of the complex time correlation image data, first, the phase of the pixel value (g _ω (i, j)) of the pixel to be processed is set. In order to multiply by 8, the pixel value of the pixel to be processed needs to be raised to the eighth power.

Then, pixel values g _ω (i−1, j−1), g _ω (i−1, j), g _ω (i−1, j + 1), adjacent to the periphery of the pixel to be processed, To obtain the sum of the phases of g _ω (i, j−1), g _ω (i, j + 1), g _ω (i + 1, j−1), g _ω (i + 1, j), g _ω (i + 1, j + 1) In addition, it is necessary to multiply all the pixel values of these eight pixels.

  Then, in order to obtain the difference between the phase of the pixel value of the pixel to be processed that has been multiplied by eight and the sum of the phases of the eight pixels adjacent to the periphery of the pixel to be processed, It is necessary to divide the pixel value raised to the eighth power by the product of the pixel values of the eight adjacent pixels around the pixel to be processed.

  When the above three operations are expressed by mathematical expressions, the following expressions (10) to (12) are obtained.

The reason why g _omega (i, j) are 9 square instead of 8 squares in the equation (10) is because g _omega (i, j) is included in the multiplication in equation (11). As a result, the exponent part of the value obtained by dividing the value of Expression (10) by the value of Expression (11) is obtained by multiplying the phase of the pixel value g _ω (i, j) of the pixel to be processed by eight times. This corresponds to the difference between the phase sum of eight pixels adjacent to the periphery of the target pixel.

  In Expression (12), only the phase component (arg) is extracted after dividing the value of Expression (10) by the value of Expression (11). Due to the nature of the phase, the data obtained by equation (12) is folded in the range of −π to π.

  If the phase-only Laplacian process based on the above equations (10) to (12) is executed for all the pixels of the complex time correlation image data, the Laplacian image is image data obtained by second-order differentiation of only the phase component in the complex time correlation image data. Data can be obtained.

  FIG. 14 is a diagram illustrating an example of a Laplacian image obtained by imaging Laplacian image data obtained by performing phase-only Laplacian processing on complex time correlation image data corresponding to the phase image 1100 of FIG. 11. The Laplacian image 1400 in FIG. 14 does not have a periodic edge portion as seen in the phase image 1100 in FIG. 11, and a local abnormal portion 1401 clearly appears. Therefore, according to the Laplacian image 1400 of FIG. 14, only the abnormal portion 1401 can be easily detected without being affected by the above-described phase jump only by performing a normal detection process using a threshold or the like. it can.

  By the way, the calculation method based on the above-described equations (10) to (12) is based on the premise that the complex time correlation image data that is the basis of the Laplacian image is handled as complex data as it is. As mentioned above, complex data is basically handled separately as real part data and imaginary part data, so complex time correlation image data is treated as complex data as it is and phase-only Laplacian processing is executed. Then, it takes a long time to complete the process.

  Therefore, the image processing unit 106 according to the embodiment performs Laplacian image data (by using a phase-only Laplacian process) by performing arithmetic processing using a new method different from the method of handling complex time correlation image data as it is as complex data. Is obtained at a higher speed.

  First, the image processing unit 106 executes a process of extracting a phase component from complex time correlation image data based on the above equation (9). Data obtained by this processing corresponds to the pixel value of the phase image data, and the pixel value is a real number folded in the range of −π to π.

  Next, the image processing unit 106 performs a normal Laplacian process for an image having a normal real pixel value on the phase image data obtained based on the above equation (9). That is, since the pixel value of the phase image data obtained based on the above equation (9) is a real number, the phase image data can be handled in the same way as image data having a normal real pixel value.

  In the embodiment, a predetermined pre-processing may be performed before the Laplacian processing.

  In the above Laplacian process, the pixel value after the process may be out of the range of −π to π. However, due to the nature of the phase, a certain phase (for example, φ1) and a certain phase obtained by adding N times the period 2π (= φ1 + 2π × N) can be regarded as the same.

  Therefore, the image processing unit 106 according to the embodiment executes a remainder calculation process modulo 2π on the data after the Laplacian process, that is, a process of calculating a remainder obtained by dividing the data to be processed by 2π. Data folded in the range of −π to π is obtained. According to this process, it is possible to obtain a result equivalent to the process (phase-only Laplacian process) for second-order differentiation of only the phase component in the complex time correlation image data.

  As described above, in the embodiment, for real data (phase image) obtained from complex data (complex time correlation image), normal Laplacian processing and remainder calculation similar to those for handling normal real images are performed. By performing image processing including processing, it is possible to obtain image data equivalent to a Laplacian image obtained by performing phase-only Laplacian processing on complex number data (complex time correlation image).

  Furthermore, in the embodiment, since the target of calculation in image processing is only normal real data, complex data that requires simultaneous consideration of two types of data, the real part and the imaginary part, is the target of calculation. A result (Laplacian image) can be obtained in a shorter time than the phase-only Laplacian process.

  In the embodiment, the filter size and filter coefficient of a Laplacian filter used for Laplacian processing are not limited to the example shown in FIG. For example, in the embodiment, a 3 × 3 Laplacian filter (a so-called 4-neighbor Laplacian filter) is used for taking a difference between a pixel to be processed and four pixels adjacent to the pixel to be processed vertically and horizontally. Alternatively, in addition to the eight pixels adjacent to the periphery of the pixel to be processed, a 5 × 5 Laplacian filter that takes into account the information of the 16 pixels further adjacent to the periphery of the eight pixels may be provided. May be used.

  Hereinafter, processing executed in the embodiment will be described.

  FIG. 15 is a flowchart illustrating a series of processing executed when the inspection system according to the embodiment inspects the inspected object 150. In the following, it is assumed that the inspected object 150 is already fixed to the arm 140 and arranged at the initial position of the inspection.

  As shown in FIG. 15, the PC 100 according to the embodiment first outputs a fringe pattern for inspecting the inspected object 150 to the illumination device 120 (S1501).

  The illuminating device 120 stores the fringe pattern input from the PC 100 (S1521). And the illuminating device 120 displays the stored fringe pattern so that it may change according to a time transition (S1522). Note that the condition for starting display by the illumination device 120 is not limited to storing the fringe pattern, and may be that the inspector performs a start operation on the illumination device 120, for example.

  Then, the PC 100 transmits an imaging start instruction to the time correlation camera 110 (S1502).

  Next, the time correlation camera 110 starts imaging the inspection object 150 and a region including the periphery of the inspection object 150 in accordance with the transmitted imaging start instruction (S1511). Then, the control unit 240 of the time correlation camera 110 generates intensity image data and complex time correlation image data (S1512). Then, the control unit 240 of the time correlation camera 110 outputs the intensity image data and the complex time correlation image data to the PC 100 (S1513).

  The image data acquisition unit 104 of the PC 100 receives the intensity image data and the complex time correlation image data (S1503). Then, the image processing unit 106 of the PC 100 executes the following image processing based on the received intensity image data and complex time correlation image data (S1504).

  FIG. 16 is a flowchart showing details of image processing executed by the inspection system (the image processing unit 106 of the PC 100) according to the embodiment.

  As shown in FIG. 16, in the embodiment, first, the image processing unit 106 converts phase image data, which is real number data, from complex time correlation image data, which is complex number data, based on the above equation (9). Extract (S1601).

  Then, the image processing unit 106 performs Laplacian processing as processing for performing second-order differentiation on an image having a normal real pixel value on the phase image data extracted in S1601 (S1602).

  Then, the image processing unit 106 performs a remainder calculation process using 2π as a modulus for each pixel value of the image data after the process of S1602 (S1603). As a result, the result of the second-order differentiation process in S1602 is folded in the range of −π to π, and the Laplacian image data as a result equivalent to the phase-only Laplacian process for second-order differentiation of only the phase component in the complex time correlation image data is obtained. Can be obtained.

  When the process of S1603 ends, the image process of S1504 in FIG. 15 ends, and the process proceeds to S1505. Then, the abnormality detection processing unit 107 of the PC 100 detects an abnormality as a portion where the phase changes locally by performing threshold processing on the Laplacian image data obtained by the image processing.

  Then, the control unit 103 outputs the detection result as a result of the processing of S1505 to, for example, a display device (not shown) included in the PC 100 (S1506). As an output method to the display device, for example, there is a method of displaying an intensity image and displaying an area determined to be a defect in the intensity image so that the inspector can recognize an abnormality. It is done. In the embodiment, the determination result is not limited to visual output, and the determination result may be output by voice or the like.

  Then, the control unit 103 determines whether or not the inspection of the inspected object 150 has ended (S1507).

  When it is determined that the inspection of the inspected object 150 has not been completed (S1507: No), the arm control unit 101 can capture the surface of the inspected object 150 to be inspected with the time correlation camera 110. Then, arm movement control is performed according to a predetermined setting (S1508). When the arm movement control ends, the control unit 103 transmits an imaging start instruction to the time correlation camera 110 again (S1502).

  On the other hand, when it is determined that the inspection of the inspected object 150 has ended (S1507: Yes), the control unit 103 outputs an end instruction for notifying that the inspection has ended to the time correlation camera 110 ( S1509). As a result, a series of processes executed by the PC 100 is completed.

  Then, the time correlation camera 110 determines whether an end instruction has been received (S1514). If an end instruction has not been received (S1514: No), the processing from S1511 is performed again. On the other hand, when an end instruction is accepted (S1514: Yes), the process ends.

  Note that the inspecting process of the illumination device 120 may be performed by an inspector, or may be ended in accordance with an instruction from another configuration.

  With the above, a series of processes executed when the inspection system of the embodiment inspects the object to be inspected is completed.

  In the above-described embodiment, the example in which the time correlation camera 110 having the configuration illustrated in FIG. 2 is used to generate the intensity image data and the time correlation image data has been described. However, the generation of the intensity image data and the time correlation image data is not limited to the time correlation camera 110 having the configuration shown in FIG. It can also be realized by an imaging system which

  For example, if image data generated by a normal digital still camera is output and the information processing apparatus uses the image data generated by the digital still camera as frame image data and superimposes a reference signal, the same time as in the embodiment Correlated image data can be obtained, and even when a digital camera that superimposes a reference signal on a light intensity signal in an image sensor is used, time correlated image data similar to that of the embodiment can be obtained.

  Hereinafter, the results of experiments conducted to confirm the accuracy of the results obtained by the image processing of the embodiment will be described. More specifically, a Laplacian image obtained by image processing according to an embodiment in which Laplacian processing and remainder calculation processing are performed on real phase image data extracted from complex complex-time correlation image data, and complex complex-time correlation The result of an experiment performed to confirm that the Laplacian image obtained by performing phase-only Laplacian processing on the image data matches will be described.

  FIG. 17 is a diagram illustrating an example of the phase image used in the experiment for confirming the accuracy of the result obtained by the image processing according to the embodiment. When the result of applying the phase-only Laplacian process to the complex time correlation image data corresponding to the phase image 1700 of FIG. 17 is imaged, a Laplacian image 1800 as shown in FIG. 18 is obtained, and the phase image 1700 of FIG. When the image processing result of the embodiment including the Laplacian process and the remainder calculation process is imaged on the phase image data, a Laplacian image 1900 as shown in FIG. 19 is obtained.

  If the image processing of the embodiment is equivalent to the phase only Laplacian processing, the Laplacian image 1800 shown in FIG. 18 and the Laplacian image 1900 shown in FIG. 19 should match. Therefore, in order to confirm the degree of coincidence between the Laplacian image 1800 shown in FIG. 18 and the Laplacian image 1900 shown in FIG. Image 2000 was obtained.

  An image 2000 shown in FIG. 20 has a uniform gradation for all pixels. This is because the Laplacian image 1800 shown in FIG. 18 and the Laplacian image 1900 shown in FIG. 19 completely match, and the difference between them for all pixels becomes zero.

  In this way, it was confirmed that the image processing of the embodiment is equivalent to the phase-only Laplacian processing, and the result obtained by the image processing of the embodiment was confirmed to be accurate.

  As described above, the arithmetic processing unit 105 of the embodiment is based on Laplacian image data obtained by performing Laplacian processing and remainder arithmetic processing on real phase image data extracted from complex complex time correlation image data. Detect anomalies. As a result, since only real data is subject to calculation in image processing, a result equivalent to performing phase-only Laplacian processing on complex complex-time correlated image data can be obtained at higher speed.

  Hereinafter, some modified examples of the embodiment will be described.

<First Modification>
In the above-described embodiment, the example in which the abnormal region is specified based on the local change (difference from the surroundings) of the phase, amplitude, and intensity has been described. However, the abnormal region is specified based on the difference from the surroundings. It is not limited to. For example, as a first modification, an abnormal region may be specified based on a difference from reference shape data (reference data) set (acquired) in advance. In this case, it is necessary to match the position and synchronization status of the spatial phase modulation illumination (stripe pattern) with the status when the reference data is set (acquired).

  The first modified example is a phase image obtained by capturing a phase image, an amplitude image and an intensity image obtained from a reference surface, and an actual inspected object, which are stored in advance in a storage unit (not shown). The amplitude image and the intensity image are compared, and whether there is a difference more than a predetermined standard for any one or more of the phase, amplitude, and intensity of the light between the surface of the object to be inspected and the reference surface Determine whether.

  Hereinafter, in the first modification, an inspection system having the same configuration as that of the embodiment is used, and the surface of a normal inspection object is used as an example of the reference surface.

  In this case, while the illumination device 120 irradiates the fringe pattern via the screen 130, the time correlation camera 110 images the surface of a normal object to be inspected, and intensity image data, time correlation image data, Is generated. Then, the PC 100 generates a phase image, an amplitude image, and an intensity image from the intensity image data and the time correlation image data generated by the time correlation camera 110, and generates the generated phase image in a storage unit (not shown) of the PC 100. The amplitude image and the intensity image are stored.

  Then, the time correlation camera 110 captures an object to be inspected to determine whether or not there is a possibility of abnormality, and generates intensity image data and time correlation image data. Then, the PC 100 generates a phase image, an amplitude image, and an intensity image from the intensity image data and the time correlation image data, and then stores the phase image, the amplitude image, and the intensity image of a normal inspection object stored in the storage unit in the past. And compare.

  In the above comparison, the PC 100 determines that the comparison result between the phase image, amplitude image, and intensity image of a normal object to be inspected and the phase image, amplitude image, and intensity image of the object to be inspected are abnormal. It is assumed that data representing characteristics for specifying a region is output. Thereby, the PC 100 identifies each abnormal region of the phase image, the amplitude image, and the intensity image by identifying pixels (regions) whose characteristics for identifying the abnormal region are equal to or greater than a predetermined reference. be able to.

  According to the above procedure, in the first modification, it is possible to determine whether or not there is a difference from the surface of a normal inspection object, in other words, whether or not there is a possibility that an abnormality exists on the surface of the inspection object. Note that any method may be used as a method for comparing the phase image, the amplitude image, and the intensity image, and the description thereof will be omitted.

  In the first modification, an example in which data indicating characteristics for extracting an abnormal region is output based on a difference from the reference surface has been described. However, it is also conceivable to identify the abnormal region by combining the technique using the difference in the reference surface described in the first modification and the technique using the difference between the surroundings described in the embodiment. Note that any technique may be used as a technique for combining the technique of the first modification and the technique of the embodiment, and the description thereof is omitted here.

<Second Modification>
In the above-described embodiment, an example in which an abnormality (defect) of an inspection object is detected by moving a stripe pattern in the x direction has been described. However, when an abnormality (defect) in which the normal distribution changes sharply in the y direction perpendicular to the x direction is generated in the inspection object, the fringe pattern is moved in the y direction rather than in the x direction. It may be easier to detect defects. Therefore, as a second modification, an example in which a fringe pattern moving in the x direction and a fringe pattern moving in the y direction are alternately switched will be described.

  The illumination control unit 102 of the second modified example switches the stripe pattern output to the illumination device 120 at every predetermined time interval. Thereby, the illuminating device 120 outputs the some fringe pattern extended in the different direction with respect to one test object surface.

  FIG. 21 is a diagram illustrating a switching example of a fringe pattern output from the illumination control unit 102 according to the second modification. As shown in FIG. 21A, the illumination control unit 102 according to the second modification first causes the fringe pattern displayed by the illumination device 120 to transition in the x direction. Thereafter, as illustrated in FIG. 21B, the illumination control unit 102 according to the second modification causes the stripe pattern displayed by the illumination device 120 to transition in the y direction.

  Then, the control unit 103 of the PC 100 performs abnormality detection based on the time correlation image data obtained from the stripe pattern irradiation of FIG. 21A, and is obtained from the stripe pattern irradiation of FIG. Abnormality detection is performed based on the time correlation image data.

  FIG. 22 is a diagram illustrating an example in which the illumination control unit 102 according to the second modification irradiates a surface including an abnormality (defect) 2201 with a stripe pattern. In the example shown in FIG. 22, an abnormality (defect) 2201 extends in the x direction. In this case, the illumination control unit 102 sets the fringe pattern to move in the y direction that intersects the x direction, in other words, the direction that intersects the longitudinal direction of the abnormality (defect) 2201. With this setting, detection accuracy can be improved.

  FIG. 23 shows the relationship between the abnormality (defect) 2201 and the stripe pattern on the screen 130 when the stripe pattern is changed in the y direction in FIG. 22, in other words, the direction orthogonal to the longitudinal direction of the abnormality (defect) 2201. FIG. When an abnormality (defect) 2201 having a narrow width in the y direction and a longitudinal direction in the x direction that intersects the y direction occurs, the light emitted from the illumination device 120 is in the y direction that intersects the x direction. The cancellation of light amplitude is increased. Therefore, as shown in FIG. 23, the PC 100 according to the second modification detects the abnormality (defect) 2201 from the amplitude image data corresponding to the fringe pattern moved in the y direction.

  In the inspection system of the second modified example, when the longitudinal direction of the defect generated in the inspection object is random, stripe patterns are formed in a plurality of directions (for example, the x direction and the y direction intersecting the x direction). By displaying, the defect can be detected regardless of the shape of the defect, and the abnormality (defect) detection accuracy can be improved. Further, by projecting a fringe pattern that matches the shape of the abnormality, the abnormality detection accuracy can be improved.

<Third Modification>
Furthermore, the technique of the embodiment is not limited to the technique of switching the fringe pattern in order to perform abnormality detection in the x direction and abnormality detection in the y direction as in the second modification described above. Therefore, as a third modification, an example in which the fringe pattern output from the illumination control unit 102 to the illumination device 120 is simultaneously moved in the x direction and the y direction will be described.

  FIG. 24 is a diagram illustrating an example of a fringe pattern output from the illumination control unit 102 of the third modified example to the illumination device 120. In the example illustrated in FIG. 24, the illumination control unit 102 moves the fringe pattern in the direction 2401.

  The stripe pattern shown in FIG. 24 includes a stripe pattern with one period 2402 in the x direction and a stripe pattern with one period 2403 in the y direction. That is, the stripe pattern shown in FIG. 24 has a plurality of stripes extending in the intersecting directions having different widths. Here, in the third modified example, it is necessary to make the width of the stripe pattern in the x direction different from the width of the stripe pattern in the y direction. Thereby, when generating the time correlation image data corresponding to the x direction and the time correlation image data corresponding to the y direction, the corresponding reference signals can be made different. In addition, since the period (frequency) of the light intensity change by the stripe pattern only needs to be changed, the moving speed of the stripe pattern (stripe) may be changed instead of changing the width of the stripe.

  Then, the time correlation camera 110 generates time correlation image data corresponding to the stripe pattern in the x direction based on the reference signal corresponding to the stripe pattern in the x direction, and based on the reference signal corresponding to the stripe pattern in the y direction. Thus, time correlation image data corresponding to the stripe pattern in the y direction is generated. Then, the control unit 103 of the PC 100 detects abnormality based on the time correlation image data corresponding to the stripe pattern in the x direction, and then detects abnormality based on the time correlation image data corresponding to the stripe pattern in the y direction. I do. Thereby, in the 3rd modification, it becomes possible to detect regardless of the direction where a defect has occurred, and the detection accuracy of abnormality (defect) can be improved.

  Note that the inspection program executed by the PC 100 according to the above-described embodiment and modification is a file in an installable format or an executable format, and is a CD-ROM, flexible disk (FD), CD-R, DVD (Digital Versatile Disk). And the like recorded on a computer-readable recording medium.

  The inspection program executed by the PC 100 according to the embodiment and the modification described above may be provided by being stored on a computer connected to a network such as the Internet and downloaded via the network. Further, the inspection program executed on the PC 100 according to the above-described embodiment may be provided or distributed via a network such as the Internet.

  As mentioned above, although embodiment and the modification of this invention were described, these embodiment and the modification are shown as an example and are not intending limiting the range of invention. These novel embodiments and modifications can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and equivalents thereof.

  DESCRIPTION OF SYMBOLS 100 ... PC, 104 ... Image data acquisition part, 105 ... Operation processing part, 110 ... Time correlation camera, 120 ... Illumination device (illumination part), 130 ... Screen (illumination part).

Claims (2)

A planar illumination unit that provides periodic temporal and spatial changes in light intensity;
An image data acquisition unit that acquires complex time correlation image data from a time correlation camera or an imaging system that performs an equivalent operation;
An arithmetic processing unit that detects an abnormality based on Laplacian image data obtained by performing Laplacian processing and remainder arithmetic processing on phase image data extracted from the complex time correlation image data;
An inspection system comprising: Complex time correlation image data is acquired from a time correlation camera that images a test object illuminated by a planar illumination unit that gives periodic temporal changes in light intensity and spatial changes, or an imaging system that performs an equivalent operation. An image data acquisition step;
An arithmetic processing step for detecting an abnormality based on Laplacian image data obtained by performing Laplacian processing and remainder arithmetic processing on phase image data extracted from the complex time correlation image data;
An inspection method comprising: