TWI447379B - Image processing computer program product, detection apparatus and detection method for internal defects in solar cells - Google Patents

Description

Computer program product, detection device and detection method for image processing of internal defects of solar battery

The invention relates to a computer program product, a detection device and a detection method which can be used for internal defects of a solar battery, in particular to image processing and automatic detection technology.

World civilization With the rapid development of science and technology, various energy sources have been discovered and adopted, among which oil is the leader. In recent years, with the world population skyrocketing, the stock of oil has rapidly declined, and solar energy has become one of the key projects of energy science and technology development in many countries. A considerable amount of funds have been used to subsidize this industry. Solar cells can directly convert light energy into direct current. Its characteristics are fuel-free, waste-free and pollution-free, no rotating components and noise, and its appearance and size can be changed arbitrarily. The service life can be as long as 20 years, so it is suitable for consumption. Sex products or general household use.

In response to the trend of such new energy, many manufacturers in the country have invested in the solar industry. Since most of the domestic manufacturers are mainly manufacturing, how to effectively improve production efficiency and reduce production costs has become an important issue. Among them, it is conceivable that the yield test of solar cells is a very important technical focus.

At present, many researches on the outside of the solar cell are taken by the camera, and then image processing is used to automatically detect or classify. However, in addition to the visible external sputum, the solar cell has some internal 瑕疵 from the battery. The flaw caused by internal micro-cracking. Since these internal flaws occur inside the solar panel, they cannot be directly judged by the naked eye, and the existing automatic detection system cannot detect them. However, due to the internal flaws of internal micro-cracking, the efficiency of power generation of the entire solar panel is lowered, and the panel may be broken after the product is used for a period of time, so it is actually a technical problem that must be solved.

Accordingly, one aspect of the present invention is a computer program product, a detection device, and a detection method for providing image processing for internal defects of a solar cell.

According to an embodiment of the invention, a computer program product for image processing is used for detecting internal defects of a solar cell, and at least performing the following steps: a reading step, first to fourth processing steps, and an output step. The reading step reads in the image, and the image contains a plurality of pixels. The first processing step uses the maximum threshold and the minimum threshold to compare with the pixels to filter a plurality of noise pixels of the image to generate a plurality of candidate pixels. The second processing step compares the candidate pixels to the inner frame threshold to identify a plurality of inner frame pixels in the candidate pixels. The third processing step sets the intensity of the inner frame pixel to be greater than the defect threshold. The fourth processing step sequentially compares the candidate pixels including the inner frame pixels with the defect threshold to find a plurality of defective pixels. And the output step outputs the corresponding position of the defective pixel.

According to the above embodiment, the computer program product can further perform a slope calculation step for finding the edge four points of a region composed of candidate pixels to calculate the slope of the region before the second processing step. The above computer program product can also output the image data to a display interface. The so-called noise pixels can include a plurality of background pixels.

According to another embodiment of the present invention, a detecting device for performing internal defects of a solar cell such as the aforementioned computer program product is also proposed. The detecting device comprises a power supply, a charge coupled camera, a computer host, a computer program product as described above, and a display device. The power supply supplies a DC bias to the solar cells, causing the solar cells to emit spectra of various wavelengths. A charge coupled camera includes a filter lens that filters visible light into a charge coupled camera and a lens that is configured to oppose the solar cell to capture a spectral photo of the solar cell. The host computer is electrically connected to the charge coupled camera to receive the spectral photo and convert it into an image, and executes the aforementioned computer program product for image processing. The display device is electrically connected to the host computer and displays the corresponding position of the defective pixel.

According to still another embodiment of the present invention, a method for detecting internal defects of a solar cell is also proposed. The detection method includes at least a biasing step, an imaging step, a dumping step, and an image processing step. The biasing step refers to the bias circuit supplying a bias voltage to the solar cell to cause the solar cell to emit a spectrum. The imaging step refers to taking a spectral picture of the solar cell by filtering the visible light. The transfer step refers to the operation unit storing the spectrum photo as an image composed of a plurality of pixels. The image processing step refers to the operation unit using the maximum threshold value and the minimum threshold value to compare with the pixels to filter a plurality of noise pixels of the image to generate a plurality of candidate pixels, and the operation unit compares the candidate pixels with the inner frame threshold value. Recognizing a plurality of inner frame pixels in the candidate pixels, the operation unit further sets the intensity of the inner frame pixels to be greater than a defect threshold, and the operation unit finally compares the candidate pixels including the inner frame pixels with the defect threshold sequentially to find the complex number. Defective pixels.

According to the above embodiment, the detecting method may include the step of searching for four edges of the candidate pixel after the computing unit generates a plurality of candidate pixels to calculate a slope between the candidate pixels. The detecting method may also include the step of outputting an image on the display interface or the step of outputting the corresponding position of the defective pixel. The aforementioned noise pixel includes a plurality of background pixels.

In summary, the computer program product, the detecting device and the detecting method for image processing of the internal defects of the solar cell proposed by the present invention can easily detect the internal flaw during the production process, and further according to the internal flaw. The location that occurs, improving the process of its production line. In this way, the quality and yield of solar cell manufacturing can be efficiently improved.

The computer program product, the detecting device and the detecting method for image processing of internal defects of the solar cell proposed by the present invention can be used to detect internal defects of the solar cell as shown in FIG. 1 , and the solar energy can be known in FIG. 1 The battery 100 includes 2 x 9 solar cells 110, and each solar cell 110 is separated by an inner frame 120.

The detecting device according to an embodiment of the present invention comprises a power supply (a conventional DC voltage supply), a charge coupled camera, a computer host, a computer program product for image processing, and a display device (such as a computer screen). The power supply supplies a DC bias to the solar cells, causing the solar cells to emit spectra of various wavelengths. A charge-coupled camera consists of a filter lens that filters out visible light into a charge-coupled camera (see Figure 2, which shows that the spectrum can only pass through a wavelength above 800 nm) and the spectrum of the solar cell that is configured to oppose the solar cell. The lens of the photo (such as the 16-bit grayscale CCD camera, which can resolve 765×510 images), in order to clearly capture the spectral photos of the solar cells with weaker intensity, the exposure time needs at least 30 seconds.

Then, the host computer is electrically connected to the charge coupled camera to receive the spectrum photo and convert it into an image, and execute a computer program product for image processing. The display device is electrically connected to the host computer and displays the corresponding position of the defective pixel.

Please refer to Figure 3 for the flow chart of the above-mentioned computer program products for image processing. The details are as follows:

Step 310: The reading step reads the image, and the image includes a plurality of pixels.

Step 320: The first processing step uses a maximum threshold value and a minimum threshold value to compare with the pixels to filter a plurality of noise pixels of the image to generate a plurality of candidate pixels.

Step 330: The second processing step compares the candidate pixels with the inner frame threshold to identify a plurality of inner frame pixels in the candidate pixels.

Step 340: The third processing step sets the intensity of the inner frame pixel to be greater than the defect threshold.

Step 350: The fourth processing step sequentially compares the candidate pixels including the inner frame pixels with the defect threshold to find a plurality of defective pixels.

Step 360: The output step outputs a corresponding position of the defective pixel.

In more detail, step 320 (double-critical noise filtering) selects an N×N mask for each pixel in the image, and uses a minimum threshold for the luminance value of each pixel in the mask. The value T1 is compared with the maximum threshold T2 (T1<T2). If the grayscale value of most of the pixels in the mask is closer to the minimum threshold T1, then the pixel is determined to be in the background, so the grayscale value of the central pixel is masked. Set to black; conversely, if the grayscale value of most of the pixels in the mask is closer to the maximum threshold T2, then the pixel is determined to be located in the solar cell, so the grayscale value of the central pixel of the mask is kept unchanged. The detailed algorithm is as follows:

A. Set the minimum threshold T1 and the maximum threshold T2, let T1 < T2; cnt1 = cnt2 = 0

B. Calculate the absolute value d1, d2 of the difference between T1 and T2 for each pixel grayscale value in the mask.

C. Update cnt1 and cnt2 based on the size of d1 and d2. If d1 is smaller than d2, then cnt1 is increased by 1; otherwise cnt2 is added 1

D. The output pixel value is determined by the values of cnt1 and cnt2. If cnt1>cnt2, the gray level of the central pixel in the mask is black; otherwise, the original value remains unchanged.

After step 320, the removed noise pixels also include background pixels. That is to say, the candidate pixels generated after the step 320 will be the image pixel set corresponding to the solar cell 100.

If the steps 330-340 are further described in detail, the main purpose of the two steps is to identify the black inner frame formed by the patchwork of the solar cell unit 110, that is, the corresponding pixel of the inner frame 120. By step 330-340, the inner frame is prevented from being misinterpreted as an internal defect. Steps 330-340 are to select an inner frame threshold T3, and gradually scan the horizontal or vertical line in the graph, compare T3 with the grayscale value of each scan pixel, and determine whether a grayscale value of a large number of pixels in the scan line is smaller than T3, if yes, change the color of all pixels in the scan axis to white; otherwise, retain the original grayscale value of the pixel. The detailed algorithm is as follows:

A. Set the inner frame threshold T3 and K

B. Gradually grab the grayscale value x(i,j) of each pixel of the horizontal axis in the candidate pixel and compare it with the critical value T3. If x(i,j) is less than T3, then cnt3 is added 1

C. If cnt3>K, it is determined that the pixel of the horizontal axis is the inner frame pixel, so the horizontal axis pixel value is set to white (on the scale of 0~255, the white is 255, the intensity is necessarily greater than the defect threshold)

D. Repeat (B) and (C) for the vertical axis in the image to identify the vertical line of the inner frame

To further detail step 350 (regional growth method), the candidate pixels including the inner frame pixels are sequentially compared with the defect threshold to find a plurality of defect images. The detailed algorithm is as follows:

A. Let C be the internal defect area that has been found so far, initialize C=0

B. sequentially look for pixels whose intensity is less than the defect threshold θ and have not been classified into any existing internal defect regions; if there is no such pixel, leave; otherwise, let C=C+1, and classify the pixel at Internal defect area C, this pixel is added to a blank set S

C. Find the adjacent pixel p in S. If the strength of p is also smaller than the defect threshold θ and has not been classified into any existing internal defect regions, p is classified into the internal defect region C, and the pixel adjacent to p is added to S.

D. Repeat (C) to find all pixels p belonging to S

E. Back to (B)

On the other hand, when the image read in step 310 is non-parallel to the window, or the image is not positive and oblique, the computer program product proceeds to step 325 after step 320 and before step 330, that is, A slope calculation step is performed that finds four edges of the region consisting of the candidate pixel to calculate the slope of the region. This assists in finding the position of the inner frame pixels parallel to the area.

In the process of image processing of the computer program product, the image data can be output to the display interface to display the corresponding position of the defective pixel. Example 1 of Figures 4-7, which represents the forward spectrum photo of the solar cell of Figure 1, passes through the images of steps 310-360. The embodiment 2 of FIGS. 8-10 represents an oblique spectrum photo of the solar cell of FIG. 1 through the images of steps 310-360 (including step 325). Moreover, in the instant display window, functions such as intensity/coordinate of each pixel can be added to meet the requirements of each application device.

Example 1

Please refer to Figure 4, it can be seen that after the exposure time, the internal cracks cause the spectral emission of the solar cell to emit light, so there is a shadow in the image, showing the location of the crack, and the interference of the noise is very obvious. There is no way to directly detect the action. Figure 5 is a fourth image of the image of step 320. It can be seen that step 320 not only clears the noise, but also identifies the position of the solar cell in the image. Figure 6 is a fourth image of the image of steps 330 and 340. It can be seen that after the inner frame is recognized and set to white, the difference between the inner frame and the inner frame is very obvious, and the misjudgment of the next step can be avoided. Finally, Figure 7 is a fourth image through the image of step 350, and it is undoubted that the position of the internal defects of the solar cell has been effectively found.

Example 2

Please refer to Figure 8. It can be seen that after the exposure time, the internal cracks cause the spectral scatter in the solar cell to be ineffective. Therefore, there are shadows in the image, showing the location of the crack, and the noise interference is very obvious. There is no way to directly detect the motion, and because the solar cell does not correct the coordinates with the lens, the image is skewed. Figure 9 is the image of steps 320, 325, 330, and 340 in Figure 8. It can be seen that the noise has been filtered out, and the inner frame has been identified and set, and the next step can be performed. Finally, Figure 10 is the image of step 350 through the image of step 350, and it is undoubted that the position of the internal defects of the solar cell has been effectively found.

It can be seen from the above embodiments of the present invention that the computer program product, the detecting device and the detecting method for image processing of the internal defects of the solar battery proposed by the present invention can easily detect the internal flaw during the production process, and further Improve the production process of the production line based on the location of the internal defects. In this way, the quality and yield of solar cell manufacturing can be efficiently improved.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention, and the present invention can be modified and modified without departing from the spirit and scope of the present invention. The scope is subject to the definition of the scope of the patent application attached.

100. . . Solar battery

110. . . Solar cell

120. . . Inner frame

310~360. . . step

The above and other objects, features, advantages and embodiments of the present invention will become more apparent and understood.

Fig. 1 is a schematic view showing the structure of a conventional solar cell.

Fig. 2 is a view showing a filter pattern of a filter used in an embodiment of the present invention.

3 is a flow chart showing the execution steps of a computer program product according to an embodiment of the present invention.

Figure 4 is a forward spectrum photograph of the solar cell of Figure 1 (step 310).

Figure 5 is an image of the fourth step through step 320.

Figure 6 is an image of the fourth through the steps 330, 340.

Figure 7 is an image of the fourth step through step 350.

Figure 8 is an oblique spectrum photograph of the solar cell of Figure 1 (step 310).

Figure 9 is an image of step 8 through steps 320, 325, 330, 340.

Figure 10 is an image of step 80 through step 350.

310~360. . . step

Claims (12)

An image processing computer program product for detecting internal defects of a solar cell, the computer program product performing at least the following steps: a reading step, reading an image, the image comprising a plurality of pixels; a first processing step, The method uses a maximum threshold and a minimum threshold to compare with the pixel to filter a plurality of noise pixels of the image to generate a plurality of candidate pixels; and a second processing step, the candidate pixel and an inner frame The threshold value is compared to identify a plurality of inner frame pixels in the candidate pixel; a third processing step of setting the intensity of the inner frame pixel to be greater than a defect threshold; and a fourth processing step, which includes the inner The candidate pixels of the frame pixel are sequentially compared with the defect threshold to find a plurality of defective pixels; and an output step of outputting the corresponding position of the defective pixel. The computer program product of claim 1, further comprising the step of: performing a slope calculation step for finding an edge four points of a region composed of the candidate pixels to calculate a slope of the region before the second processing step. The computer program product of claim 1 further includes the steps of: outputting the image data to a display interface. The computer program product of claim 1, wherein the noise pixel comprises a plurality of background pixels. A solar cell internal defect detecting device, comprising the computer program product according to claim 1, wherein the detecting device comprises: a power supply device that supplies a constant current bias to the solar battery, so that the solar battery emits various kinds of Wavelength spectrum; a charge coupled camera comprising: a filter lens that filters visible light into the charge coupled camera; and a lens configured to oppose the solar cell to capture a spectral photo of the solar cell; a computer host, Electrically connecting the charge coupled camera to receive the spectral photo and converting it into an image; the computer program product according to claim 1 is executed by the computer host to process the image; and a display device is electrically connected to the computer host Connect and display the corresponding position of the defective pixel. The detecting device of claim 5, wherein the filter lens passes only a spectrum of 800 nm or more. The detecting device of claim 5, wherein the photo of the solar cell is a spectrophotograph. A method for detecting internal defects of a solar cell, comprising: a biasing step, a bias circuit supplying a bias voltage to a solar cell to cause the solar cell to emit spectra of various wavelengths; and an imaging step, a filter lens Obtaining a visible photo of the solar cell by filtering out visible light; and performing a transfer step, wherein the computing unit stores the spectral photo as an image composed of a plurality of pixels; and an image processing step, the computing unit utilizes a maximum critical value a minimum threshold value is compared with the pixel to filter a plurality of noise pixels of the image to generate a plurality of candidate pixels, and the operation unit compares the candidate pixel with an inner frame threshold to identify the candidate pixel. a plurality of inner frame pixels, wherein the operation unit further sets the intensity of the inner frame pixel to be greater than a defect threshold, and the operation unit finally compares the candidate pixels including the inner frame pixel with the defect threshold in order to find A number of defective pixels are produced. The detecting method of claim 8, further comprising the step of: after generating a plurality of candidate pixels, searching for four edges of the candidate pixel to calculate a slope between the candidate pixels. The method of detecting the item 8 further includes the step of outputting the image on a display interface. The method of detecting the item 8, wherein the noise pixel comprises a plurality of background pixels. The method of detecting the item 8 further includes the step of outputting the corresponding position of the defective pixel.