JP4956841B2 - VEHICLE IMAGE PROCESSING DEVICE, VEHICLE, AND VEHICLE IMAGE PROCESSING PROGRAM - Google Patents

Description

  The present invention relates to an apparatus for processing an image around a vehicle acquired via an imaging unit, a vehicle, and a program for causing a computer to execute processing of the apparatus.

  Conventionally, a technique for processing an image obtained by imaging the periphery of a vehicle by an imaging unit and recognizing a lane mark such as a white line on a road on which the vehicle travels and an object such as a pedestrian or another vehicle existing around the vehicle Is known (see, for example, Patent Document 1). This technology makes it possible to present information to the driver and to control the driving of the vehicle according to the recognition result of the lane mark and the object.

In the vehicle environment recognition device of Patent Document 1, an image in front of the vehicle captured by a camera mounted on the vehicle is input for each processing cycle, a window to be processed is set in the image, and the lower end portion of the window Is used as a reference color corresponding to a road surface area (an area other than a white line in the road), and a road surface area and a non-road surface area (a white line or an area outside the road) are separated based on the reference color. The apparatus detects a white line using a straight line obtained from the left and right boundaries of the separated road surface area as an edge portion of the white line. At this time, one window is set in the image using a fixed value based on the position of the two white lines detected in the previous processing cycle or predetermined road information such as the road width and the driving lane width. The
Japanese Patent Laid-Open No. 7-200997

  However, on the road surface, for example, there may be a road surface repair mark or a local shadow. In many cases, the repaired trace is locally different in color from the surrounding road surface. In addition, a local shadow portion is also irradiated only by scattered light, and the color is added, so that the color is separated from the surrounding road surface. For this reason, in the apparatus of Patent Document 1, a portion corresponding to a local repair trace or shadow in the window is separated from the reference color and separated as a non-road surface area, and a white line is detected according to the boundary. Therefore, there is a possibility of false detection. This also applies to the luminance, not the color.

  The present invention has been made in view of such a background, and an image obtained by imaging the periphery of a vehicle is processed so as to reduce the influence of local luminance and color variation on the road surface, and the vehicle is obtained from the image. An object of the present invention is to provide a vehicle image processing device, a vehicle, and a vehicle image processing program capable of accurately detecting a lane mark on a road on which the vehicle is traveling.

  In order to achieve such an object, the vehicle image processing apparatus of the present invention processes an image acquired via an imaging means mounted on a vehicle, and displays a lane mark on a road on which the vehicle is traveling. In the vehicular image processing device to be detected, a kernel whose width in a predetermined direction is within a predetermined range larger than the width in the predetermined direction of the image portion of the lane mark with respect to the image acquired through the imaging unit. Kernel setting means for setting a size, smoothing means for smoothing the acquired image by filter processing using a smoothing kernel of the size set by the kernel setting means, and the smoothing means A degree-of-change calculating means for calculating a degree of change in the pixel value of each pixel of the acquired image with respect to the image after smoothing; and the degree of change of the acquired image is a predetermined value Pixel value replacement means for replacing the pixel value of the lower pixel with the pixel value of the pixel corresponding to the pixel of the smoothed image, and after the pixel value is replaced by the pixel value replacement means A lane mark detection means for detecting the lane mark from the image is provided (first invention).

  According to the vehicle image processing apparatus of the first aspect of the invention, the kernel size is set by the kernel setting means, and the image obtained by the smoothing means is filtered using the smoothing kernel of the set size. Smooth by processing. In the smoothing by the smoothing means, for each pixel in the image, a smoothing kernel is arranged with the pixel as the central pixel, and the average of the pixel values of all the pixels included in the range of the smoothing kernel is determined as the center. Processing to obtain the pixel value of the pixel is performed. As a result, the range of change in pixel value in the image is narrowed, and a low-contrast image is obtained.

  At this time, the road image is smoothed by using a kernel having a size set so that the width in the predetermined direction is within a predetermined range larger than the width in the predetermined direction of the image portion of the lane mark by the kernel setting means. Thus, when the kernel is arranged with the pixel of the image portion such as the lane mark, the local repair trace, and the shadow as the central pixel, the image portion of the surrounding road surface is included in the range of the kernel. Therefore, in the image after smoothing with respect to the image before smoothing, the pixel values of the local repair trace and shadow image part on the road surface, the image part of the lane mark, and the surroundings The difference from the pixel value of the image portion of the road surface is reduced.

  For this reason, when the change degree calculation means calculates the change degree of the pixel value of each pixel of the acquired image with respect to the image smoothed by the smoothing means, the change degree is calculated from the image portion of the road surface. The lane mark image portion having a large value becomes a large positive value, and the road surface image portion, a local repair trace having a smaller pixel value, or a shadow image portion has a small value (including a negative value). Therefore, the pixel value replacement unit replaces the pixel value of the pixel in which the degree of change of the acquired image is a predetermined value or less with the pixel value corresponding to the pixel of the smoothed image. Thereby, the pixel value of the pixel in the local repair trace or shadow image portion is replaced with the pixel value close to the surrounding road surface, and the pixel value of the pixel in the image portion of the lane mark is held as it is. Therefore, in the image after the pixel value is replaced by the pixel value replacement unit, the local repair trace and the shadow image part are removed, and the lane mark image part remains as it is. Therefore, the lane mark detection unit performs this replacement. By detecting the lane mark using the image after being performed, the lane mark can be detected with high accuracy.

  In the vehicular image processing apparatus according to the first aspect of the present invention, the kernel setting means sets the image portion of the predetermined area on the image as the distance of the predetermined area of the road from the vehicle increases. It is preferable to set the size of the kernel so that the size of the kernel is reduced (second invention).

  According to the vehicle image processing apparatus of the second aspect of the invention, the larger the distance from the vehicle in the predetermined area of the road in the real space, the smaller the size of the image portion of the predetermined area on the image. Therefore, by setting the kernel size so that the larger the distance from the vehicle in the predetermined area of the road, the smaller the kernel size set for the image portion of the predetermined area on the image, In each of the above positions, if the kernel is placed with the pixel of the image part such as the lane mark, local repair trace, shadow, etc. as the central pixel, make sure that the kernel area contains many image parts of the surrounding road surface be able to.

  In the vehicular image processing apparatus according to the first or second invention, the kernel setting means preferably sets the shape of the kernel to a rectangle or a trapezoid (third invention).

  According to the image processing apparatus for a vehicle of the third invention, the shape of the lane mark such as a white line or a yellow line on the road on which the vehicle is traveling is linear. Therefore, by setting the smoothing kernel to a rectangle or trapezoid, the smoothing kernel can be arranged so as to match the shape of the image portion of the lane mark.

  In the image processing apparatus for a vehicle according to the third aspect of the present invention, the kernel setting means is arranged so that the inclination of the center line of the long side of the kernel with respect to the direction on the image corresponding to the traveling direction of the vehicle becomes a predetermined angle. It is preferable to set the orientation of the kernel (fourth invention).

  According to the vehicle image processing device of the fourth aspect of the invention, the lane mark on the road on which the vehicle is traveling is imaged so as to go to the vanishing point in the direction on the image corresponding to the traveling direction of the vehicle on the image. It is assumed. Therefore, by setting the kernel direction so that the inclination of the center line of the long side of the kernel with respect to the direction on the image corresponding to the traveling direction of the vehicle becomes a predetermined angle, the direction of the image portion of the lane mark is set. A smoothing kernel can be set to match.

  In the vehicular image processing device according to any one of the first to fourth inventions, the change degree calculating means subtracts the smoothed image from the acquired image as the change degree. It is preferable to use a pixel value difference of each pixel obtained or a ratio of pixel values of each pixel obtained by dividing the acquired image by the smoothed image (fifth invention).

  According to the vehicle image processing device of the fifth aspect of the present invention, the difference between the pixel values of each pixel obtained by subtracting the smoothed image from the acquired image, or after the acquired image is smoothed The pixel value ratio of each pixel obtained by dividing by the image is a value indicating the degree of change of the pixel value. Therefore, the pixel value replacement means replaces the pixel value of the image using these differences or ratios. Can be done appropriately.

  In the vehicular image processing device according to any one of the first to fifth inventions, the image smoothed by the smoothing means is preferably an image having luminance as a pixel value (sixth invention). .

  According to the vehicle image processing apparatus of the sixth aspect of the present invention, there are a bright white or yellow lane mark image portion, a dark gray or brown road surface image portion, and a darker local repair trace or shadow image portion. Since the brightness is assumed to be different, the pixel value is replaced so that the local repair trace and the shadow image part are removed and the lane mark image part remains as it is for the image having the brightness as the pixel value. Thus, the image portion of the lane mark can be detected with high accuracy from the image after the replacement.

  Alternatively, in the vehicular image processing device according to any one of the first to fifth inventions, the image smoothed by the smoothing unit has a luminance or saturation calculated from a color component of a color image as a pixel value. It is preferable that the image is (Seventh invention).

  According to the vehicle image processing apparatus of the seventh aspect of the present invention, there are a bright white or yellow lane mark image portion, a dark gray or brown road image portion, and a darker local repair trace or shadow image portion. Since the brightness and saturation will be different, for the image with luminance or saturation as the pixel value, the local repair trace and the shadow image part are removed, and the image part of the lane mark remains as it is. By replacing the pixel value, the image portion of the lane mark can be accurately detected from the replaced image.

  Next, a vehicle according to the present invention includes an imaging unit, and has a function of detecting a lane mark on a running road by processing an image acquired via the imaging unit. Kernel setting means for setting a size of a kernel in which a width in a predetermined direction is within a predetermined range larger than a width in the predetermined direction of an image portion of a lane mark with respect to an image acquired via the imaging means; A smoothing unit that smoothes the acquired image by a filtering process using a smoothing kernel of a size set by the kernel setting unit, and an image that has been smoothed by the smoothing unit, A degree-of-change calculating means for calculating a degree of change in the pixel value of each pixel of the acquired image, and after smoothing the pixel value of the pixel in which the degree of change of the acquired image is a predetermined value or less A pixel value replacing unit that replaces the pixel value of the pixel corresponding to the pixel of the image; and a lane mark detecting unit that detects the lane mark from the image after the pixel value is replaced by the pixel value replacing unit. (Eighth invention).

  According to the vehicle of the eighth aspect, as described with respect to the image processing apparatus for a vehicle of the first aspect, the predetermined range in which the width in the predetermined direction is larger than the width in the predetermined direction of the image portion of the lane mark by the kernel setting means. The size of the kernel is set so as to be within, and the obtained image is smoothed by filtering using the smoothing kernel of the set size, so that the image before smoothing is In the smoothed image, there is a difference between the pixel values of the local repair traces and shadow image portions on the road surface, the pixel values of the lane mark image portions, and the pixel values of the surrounding road surface image portions. Decrease. Therefore, in the image after the pixel value is replaced by the pixel value replacement means, the local repair trace and the shadow image part are removed, and the lane mark image part remains as it is. By using and detecting the lane mark, the lane mark can be detected with high accuracy.

  Next, the image processing program for a vehicle according to the present invention processes the image acquired through the imaging means mounted on the vehicle and detects the lane mark on the road on which the vehicle is traveling. An image processing program for a vehicle to be executed in a predetermined range in which a width in a predetermined direction is larger than a width in the predetermined direction of an image portion of a lane mark with respect to an image acquired via the imaging unit A kernel setting process for setting a size of the kernel, a smoothing process for smoothing the acquired image by a filter process using a smoothing kernel of a size set by the kernel setting process, and the smoothing A change degree calculation process for calculating a change degree of a pixel value of each pixel of the acquired image with respect to the image after being smoothed by the conversion process; and A pixel value replacing process for replacing a pixel value of a pixel whose change degree is a predetermined value or less with a pixel value of a pixel corresponding to the pixel of the smoothed image, and a pixel value by the pixel value replacing process And a lane mark detection process for detecting the lane mark from the image after the replacement is performed (Ninth Invention).

  According to the vehicle image processing program of the ninth aspect of the invention, it is possible to cause the computer to execute a process that can achieve the effects described in regard to the first aspect of the invention.

  An embodiment of the present invention will be described with reference to FIGS. Referring to FIG. 1, a vehicle image processing apparatus 10 according to an embodiment of the present invention is used by being mounted on a host vehicle 1 (a vehicle of the present invention), and is captured by a camera 2 that captures the front of the host vehicle 1. The lane mark on the road on which the host vehicle 1 is traveling is detected from the image. The camera 2 (imaging means of the present invention) is a CCD camera or the like, and is attached to the front portion of the host vehicle 1.

  The position data of the lane mark detected by the vehicle image processing apparatus 10 is output to an ECU (Electronic Control Unit) 20 of the vehicle 1. The ECU 20 recognizes the lane of the road on which the host vehicle 1 is traveling based on the position data of the lane mark, determines the possibility that the host vehicle 1 will depart from the lane, and the host vehicle 1 A warning output process for performing a warning output (by voice output from a speaker (not shown), etc.) and a deviation from the lane of the host vehicle 1 are avoided when there is a possibility of departure from the lane. In addition, a lane departure avoidance control process for assisting driving of the brake and steering of the host vehicle 1 is executed.

  Next, referring to FIG. 2, the vehicle image processing apparatus 10 functions as an image acquisition unit 11, a kernel setting unit 12, a smoothing unit 13, a change degree calculation unit 14, and a pixel value replacement. Means 15 and lane mark detection means 16 are provided.

  The image acquisition unit 11 receives an image signal output from the camera 2 and acquires a grayscale image composed of pixel data from the input image signal. At this time, the pixel data is composed of luminance values.

  The kernel setting unit 12 sets the size of the kernel in which the width in the predetermined direction is within a predetermined range larger than the width in the predetermined direction of the image portion of the lane mark for the image acquired via the camera 2. . Details of the setting of the smoothing kernel will be described later.

  The smoothing unit 13 smoothes the image acquired via the camera 2 by a filter process using a smoothing kernel having a size set by the kernel setting unit 12. In the smoothing by the smoothing means 13, for each pixel in the image, a smoothing kernel is arranged with the pixel as the central pixel, and the average of the pixel values of all the pixels included in the range of the kernel is determined as the pixel of the central pixel. Processing to make a value is performed.

  The degree-of-change calculating unit 14 calculates the degree of change of the pixel value of each pixel of the image acquired via the camera 2 with respect to the image smoothed by the smoothing unit 13. Specifically, the change degree calculation means 14 uses the difference between the pixel values of each pixel obtained by subtracting the smoothed image from the acquired image as the change degree.

  The pixel value replacement unit 15 smoothes the pixel value of the pixel whose change degree calculated by the change degree calculation unit 14 of the image acquired via the camera 2 is equal to or less than a predetermined value by the smoothing unit 13. The pixel value of the pixel corresponding to the pixel of the subsequent image is replaced.

  The lane mark detection means 16 detects the lane mark on the road on which the host vehicle 1 is traveling from the image after being replaced by the pixel value replacement means 15. Specifically, the lane mark detection means 16 performs edge extraction processing on the replaced image and detects lane marks from the extracted edge points. At this time, the lane mark detection means 16 extracts a straight line component from the point sequence data of the edge points as lane mark candidates, and the host vehicle 1 travels based on the position on the image from among the extracted lane mark candidates. The lane mark that defines the lane of the road is detected. As a method for extracting a straight line component from the point sequence data, a method such as Hough transform or straight line fitting by the least square method can be used.

  The vehicle image processing apparatus 10 has an A / D conversion circuit that converts an input analog signal into a digital signal, an image memory that stores a digitized image signal, and accesses (reads and writes) data stored in the image memory. A computer having an interface circuit for performing various kinds of arithmetic processing on an image stored in the image memory (an arithmetic processing circuit comprising a CPU, a memory, an input / output circuit, etc., or a combination of these functions) An electronic unit composed of a microcomputer. The vehicle image processing apparatus 10 causes the computer to execute the vehicle recognition program of the present invention so that the computer acquires the image acquisition means 11, the kernel setting means 12, the smoothing means 13, and the change degree calculation means. 14, pixel value replacement means 15, and lane mark detection means 16.

  Next, the operation (vehicle image processing) of the vehicle image processing apparatus 10 will be described with reference to the flowchart shown in FIG. The process according to the flowchart shown in FIG. 3 is repeatedly executed every control cycle of the vehicle image processing apparatus 10.

In STEP 1 of FIG. 3, the image acquisition unit 11 acquires a gray scale image (luminance image) via the camera 2. The acquired luminance image is A / D converted and stored in the image memory. FIG. 4A illustrates a luminance image I ₁ acquired via the camera 2 at a certain control cycle time. The luminance image I ₁ is an image composed of m × n pixels as exemplified in FIG. Each pixel P ₁ of the luminance image I ₁ has a luminance value Y and is represented by P ₁ (i, j) = Y _ij . Here, i and j are the coordinates of each pixel, and are integers satisfying 0 ≦ i <m and 0 ≦ j <n.

  In the example of FIG. 4 (a), the host vehicle 1 is traveling in the direction of the arrow, the lane mark that defines the left side of the lane of the road on which the host vehicle 1 is traveling is the dashed white line A1, and the right side is defined. The case where the lane mark to be performed is a broken white line A2. At this time, the edge portions of the broken white lines A1 and A2 are uneven, and linear shadows (black lines) B1 and B2 are formed in these portions. Further, on the road surface of the lane of the road on which the host vehicle 1 is traveling, there are a linear repair mark (black line) B3 darker than the surroundings and a black dot group B4 due to local unevenness and dirt.

Next, in STEP 2, the kernel setting means 12 sets a plurality of sizes of smoothing kernels for the luminance image I ₁ acquired in STEP 1.

Here, with reference to FIG. 5, the setting of the smoothing kernel will be described. FIG. 5 illustrates five smoothing kernels K _{1 to} K ₅ set for the luminance image I ₁ . The smoothing kernels K _{1 to} K ₅ are used when the pixels included in the regions R _{1 to} R ₅ of the luminance image I ₁ are used as the center pixel for smoothing. The vertical size Y _f [Pixel] of the smoothing kernels K _{1 to} K ₅ is set to several pixels (for example, 1 to 3 [Pixel]). Regions R _{1 to} R ₅ are gradually narrowed in the vertical direction as they move away from the host vehicle 1. The size Y _f may be a value that decreases as the distance from the host vehicle 1 increases. The horizontal size X _f [Pixel] of the smoothing kernels K _{1 to} K ₅ is a value corresponding to the predetermined width ΔX in the real space. The predetermined width ΔX is larger than a value assumed to be the width of the lane mark (for example, 0.1 to 0.75 [m]) (for example, several times the width of the lane mark, specifically 0.5 to 1 [m]). Is used. The predetermined width ΔX is a value smaller than a value assumed to be the road width. The horizontal sizes _Xf of the smoothing kernels K _{1 to} K ₅ are, for example, 10, 20, 50, 100, and 150 [pixel], respectively. The size _Xf is set from the relationship between the real space coordinate system and the image coordinate system of the host vehicle 1 as shown in FIG. As shown in FIG. 6C, the image coordinate system has coordinates (x, y) with the upper left as the origin. Further, the physical interval per pixel is (δx, δy).

At this time, as shown in FIG. 6A, the distance Z of the object on the road surface to the host vehicle 1, the focal length f of the camera 2, the height h from the road surface of the camera 2 in real space, The physical height Y _c of the object imaged in is expressed by the following formula (1)
Y _c = f × h / Z (1)
There is a relationship. Further, the physical height Y _c , the image coordinate y of the object imaged on the image, and the physical interval δy per pixel are expressed by the following formula (2).
Y _c = y × δy (2)
There is a relationship.

Further, as shown in FIG. 6B, the distance Z of the object on the road surface to the host vehicle 1, the focal length f of the camera 2, the width ΔX of the object in real space, and the object imaged on the image The physical width X _c of the object is expressed by the following formula (3)
X _c = f × ΔX / Z (3)
There is a relationship. Further, the physical width X _c of the object imaged on the image, the image coordinates x, and the physical interval δx per pixel are expressed by the following formula (4).
X _c = x × δx (4)
There is a relationship.

From the formulas (1) to (4), the size _Xf is
X _f = (ΔX / h) × y (5)
It becomes. Since the width ΔX of the object in the real space and the height h from the road surface of the camera 2 in the real space are fixed values, the size _Xf becomes smaller as the image coordinate y becomes smaller. Furthermore, from the formulas (1) and (2), as the distance Z increases, the image coordinate y decreases, so the size _Xf also decreases. That is, the size _Xf is set to a value that decreases as the distance from the host vehicle 1 increases.

Returning to FIG. 3, next, in STEP 3, the smoothing means 13 smoothes the luminance image I ₁ using the smoothing kernels K _{1 to} K ₅ set in STEP 2. In FIG. 4 (b), it shows an image I ₂ after smoothed. Thereby, the width of the change in the pixel value in the luminance image I ₁ is narrowed, and a low-contrast image is obtained. In the smoothed image I ₂ , the pixel values of the image portions of the shadows B 1 and B 2, the repair mark B 3, and the black spot group B 4 become pixel values close to the average road surface brightness, and can be discriminated from the image portions of the surrounding road surfaces. It is gone. The pixel values of the image portions of the white lines A1 and A2 are also pixel values close to the average road surface luminance.

  Next, the processing of STEP 4 to 6 is executed for all pixels (i = 1 to m, j = 1 to n) of the image.

First, in STEP4, the change degree calculation means 14, from the pixel values of the luminance image I ₁ acquired in _{STEP1 P 1 (i, j)} , the pixel value P ₂ (i of the image I ₂ after smoothed STEP3, j ) Is subtracted to calculate the degree of change ΔI _Y (i, j) = P ₁ (i, j) −P ₂ (i, j) of the pixel value of the pixel (i, j).

Next, in STEP 5, the pixel value replacement unit 15 determines whether or not the change degree ΔI _Y (i, j) of the pixel value of each pixel is equal to or less than a predetermined value I _Yth . The predetermined value I _Yth is a value (for example, a value of 0 or less) that is determined according to a range that can be taken by the degree of change in the pixel value of the image portion of the lane mark. When the determination result in STEP 5 is YES, it is assumed that the pixel (i, j) is an image part of the road surface or an image part of shadows B1 and B2, repair mark B3, and black spot group B4 darker than the road surface. Thus, the pixel value replacing unit 15, the pixel value P ₁ of the acquired image (i, j) is replaced with the pixel value P ₂ of the smoothed image (i, j), the flow returns to STEP4. If the determination result in STEP 5 is NO, it is assumed that the pixel (i, j) is an image portion of bright white lines A1 and A2. Therefore, the pixel value replacing unit 15 returns to STEP 4 as it is without replacing the pixel value of the pixel (i, j).

FIG. 4C shows the image I ₃ after replacement, which is obtained as a result of executing the processing of STEPs 4 to 6 for all the pixels of the image. As shown in FIG. 4C, in the image I ₃ after replacement, the image portions of the shadows B1 and B2, the repair mark B3, and the black spot group B4 are replaced with the pixel values of the image I ₂ after smoothing. hand, the image portion of the white line A1, A2 are kept at the pixel value of the luminance image I _1. Therefore, in the image I ₃ after replacement, the image portions of the shadows B1 and B2, the repair mark B3, and the black dot group B4 are removed, and the image portions of the white lines A1 and A2 remain as they are.

Next, proceeding to STEP 7, the lane mark detection means 16 detects a lane mark based on the replaced image I ₃ . At this time, the image I ₃ in the shadows B1, B2 after the replacement, repaired B3, since the image portion of the black dots unit B4 is removed, only the edge points corresponding to the image portion of the white line A1, A2 by the edge extraction processing The white lines A1 and A2 are accurately detected based on the extracted edge points.

  The above is the vehicle image processing in the vehicle image processing apparatus 10 of the present embodiment. According to the present embodiment, an image obtained by capturing the periphery of the host vehicle 1 is processed so as to reduce the influence of luminance and color variations in the image, and the host vehicle 1 is traveling from the image. The white lines A1 and A2 on the road can be detected with high accuracy.

In the present embodiment, the degree-of-change calculating unit 14 uses the difference in pixel value as the degree of change. However, in other embodiments, the degree of change is obtained by dividing the acquired image by the smoothed image. It is also possible to use a ratio of pixel values of the obtained pixels (ΔI _Y (i, j) = P ₁ (i, j) / P ₂ (i, j)). In this case, the predetermined value I _Yth is a value (for example, a value of 1 or less) determined according to a range that can be taken by the degree of change in the pixel value of the image portion of the lane mark.

Further, in the present embodiment, as shown in FIG. 7, the smoothing kernels K _{1 to} K ₅ are center lines of the long sides of the kernel with respect to the direction L _c on the image corresponding to the traveling direction of the host vehicle 1. The orientation of the kernel may be set so that the inclination of L becomes a predetermined angle θ (for example, 45 °).

  In this embodiment, the shape of the smoothing kernel is rectangular. However, as another embodiment, the shape of the smoothing kernel may be trapezoidal.

  In this embodiment, the image acquisition unit 11 acquires a grayscale image via the camera 2. However, as another embodiment, the pixel data includes an R value, a G value, and a B value (or CMY output). It is also possible to acquire a color image composed of color components made up of, and the like. In this case, for example, the image acquisition unit 11 calculates the luminance value Y of the pixel by Y = α × R + β × G + γ × B from the color components (R, G, B) of the pixel of the acquired color image. Create an image. However, α, β, and γ are predetermined coefficients such that α + β + γ = 1. The image acquisition unit 11 may calculate the luminance value Y by using Y = (MAX + MIN) / 2 using the maximum value MAX and the minimum value MIN among the R value, G value, and B value. Alternatively, the image acquisition unit 11 may use a G value as the luminance value Y.

  In the present embodiment, the image acquisition unit 11 acquires a luminance image via the camera 2, the smoothing unit 13 smoothes the luminance image, and the pixel value replacement unit 14 smoothes the pixel value of the luminance image. Although the pixel value of the image is replaced, as another embodiment, for example, when the image acquisition unit 11 acquires a color image via the camera 2, the saturation of the pixel is determined from the color component of the pixel of the color image. It is also possible to calculate, create a saturation image with this saturation as a pixel value, and perform smoothing processing and pixel value replacement processing on this saturation image.

  Moreover, in this embodiment, the front of the own vehicle 1 is imaged with the camera 2, and the vehicle image processing apparatus 10 detects the lane mark from the image of the road ahead of the own vehicle 1, but other embodiments. As an example, the rear of the host vehicle 1 may be captured by a camera, and the vehicle image processing apparatus 10 may detect a lane mark from an image of a road behind the host vehicle 1.

Explanatory drawing which showed the mounting aspect to the own vehicle of the image processing apparatus for vehicles of this invention. The block diagram of the image processing apparatus for vehicles of this invention. The flowchart which shows the image process by the image processing apparatus for vehicles shown in FIG. Explanatory drawing of the image processing of FIG. FIG. 4 is an explanatory diagram regarding setting of a smoothing kernel in the image processing of FIG. 3. FIG. 6 is an explanatory diagram regarding the size of the smoothing kernel of FIG. 5. Explanatory drawing regarding the setting of the kernel for smoothing in the image processing apparatus for vehicles of other embodiment of this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Vehicle (own vehicle), 2 ... Camera (imaging means), 10 ... Image processing apparatus for vehicles, 11 ... Image acquisition means, 12 ... Kernel setting means, 13 ... Smoothing means, 14 ... Change degree calculation means, 15 ... pixel value replacement means, 16 ... lane mark detection means.

Claims (9)

In an image processing apparatus for a vehicle that processes an image acquired via an imaging unit mounted on a vehicle and detects a lane mark on a road on which the vehicle is traveling,
Kernel setting means for setting a size of a kernel in which a width in a predetermined direction is within a predetermined range larger than a width in the predetermined direction of an image portion of a lane mark with respect to an image acquired via the imaging means;
Smoothing means for smoothing the acquired image by filter processing using a smoothing kernel of a size set by the kernel setting means;
A degree-of-change calculating means for calculating the degree of change of the pixel value of each pixel of the acquired image with respect to the image smoothed by the smoothing means;
Pixel value replacement means for replacing a pixel value of a pixel in which the degree of change of the acquired image is a predetermined value or less with a pixel value of a pixel corresponding to the pixel of the smoothed image;
A vehicle image processing apparatus comprising: a lane mark detection unit that detects the lane mark from an image after the pixel value is replaced by the pixel value replacement unit. The vehicle image processing device according to claim 1,
The kernel setting means is configured to reduce the size of the kernel set for the image portion of the predetermined area on the image as the distance of the predetermined area of the road from the vehicle increases. An image processing apparatus for a vehicle characterized by setting a size. The image processing apparatus for a vehicle according to claim 1 or 2,
The vehicle image processing apparatus, wherein the kernel setting means sets the shape of the kernel to a rectangle or a trapezoid. The vehicle image processing device according to claim 3,
The kernel setting means sets the orientation of the kernel such that the inclination of the center line of the long side of the kernel with respect to the direction on the image corresponding to the traveling direction of the vehicle is a predetermined angle. An image processing apparatus for a vehicle. In the vehicle image processing device according to any one of claims 1 to 4,
The degree-of-change calculating means, as the degree of change, calculates a difference between pixel values of pixels obtained by subtracting the smoothed image from the acquired image, or smoothes the acquired image. A vehicular image processing apparatus using a ratio of pixel values of pixels obtained by division by an image after being processed. In the vehicular image processing device according to any one of claims 1 to 5,
The vehicle image processing apparatus, wherein the image smoothed by the smoothing means is an image having luminance as a pixel value. In the vehicular image processing device according to any one of claims 1 to 5,
The vehicle image processing apparatus, wherein the image smoothed by the smoothing means is an image having luminance or saturation calculated from a color component of a color image as a pixel value. A vehicle having an imaging means, having a function of detecting a lane mark on a running road by processing an image acquired through the imaging means;
Kernel setting means for setting a size of a kernel in which a width in a predetermined direction is within a predetermined range larger than a width in the predetermined direction of an image portion of a lane mark with respect to an image acquired via the imaging means;
Smoothing means for smoothing the acquired image by filter processing using a smoothing kernel of a size set by the kernel setting means;
A degree-of-change calculating means for calculating the degree of change of the pixel value of each pixel of the acquired image with respect to the image smoothed by the smoothing means;
Pixel value replacement means for replacing a pixel value of a pixel in which the degree of change of the acquired image is a predetermined value or less with a pixel value of a pixel corresponding to the pixel of the smoothed image;
A vehicle comprising: lane mark detection means for detecting the lane mark from an image after the pixel value is replaced by the pixel value replacement means. A vehicle image processing program for processing an image acquired via an imaging means mounted on a vehicle and causing a computer to execute a process of detecting a lane mark on a road on which the vehicle is running.
A kernel setting process for setting a size of a kernel in which a width in a predetermined direction is within a predetermined range larger than a width in the predetermined direction of an image portion of a lane mark for an image acquired via the imaging unit;
A smoothing process for smoothing the acquired image by a filter process using a smoothing kernel of a size set by the kernel setting process;
A change degree calculation process for calculating a change degree of a pixel value of each pixel of the acquired image with respect to the image smoothed by the smoothing process;
A pixel value replacement process for replacing a pixel value of a pixel in which the degree of change of the acquired image is a predetermined value or less with a pixel value of a pixel corresponding to the pixel of the smoothed image;
An image processing program for a vehicle having a function of causing a computer to execute a lane mark detection process for detecting the lane mark from an image after a pixel value is replaced by the pixel value replacement process.