TWI777307B - Method, computer program, and computer readable medium of using electroluminescence images to identify defect of solar cell based on deep learning technology - Google Patents

Method, computer program, and computer readable medium of using electroluminescence images to identify defect of solar cell based on deep learning technology Download PDF

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TWI777307B
TWI777307B TW109140882A TW109140882A TWI777307B TW I777307 B TWI777307 B TW I777307B TW 109140882 A TW109140882 A TW 109140882A TW 109140882 A TW109140882 A TW 109140882A TW I777307 B TWI777307 B TW I777307B
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TW202222030A (en
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林克默
林泓宏
林祐得
奕成 丹
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南臺學校財團法人南臺科技大學
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Abstract

The present invention relates to a method, a computer program, and a computer readable medium of using electroluminescence images to identify defect of solar cell based on deep learning technology. The method includes: actuating a photovoltaic module and retrieving an electroluminescence image of the photovoltaic module; performing a boundary correction process on the electroluminescence image; performing an image segmentation process on the electroluminescence image to obtain a plurality of grid-cell images; performing an AI identification process on the plurality of grid-cell images and separating said grid-cell images into defective grid-cell images and non-defective grid-cell images, so that a solar cell with flaws can be screened out. Therefore, there is no need to use a flawless image sample as the basis for comparison; instead, the above-mentioned grid-cell images are automatically identified according to the selected image feature values based on AI deep learning technology.

Description

在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的 方法、電腦程式及電腦可讀取媒體 Deep learning for solar cell defect identification in solar module images Method, computer program and computer readable medium

本發明關於一種在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法、電腦程式及電腦可讀取媒體,特別是指單晶矽太陽能電池的瑕疵辨識。 The present invention relates to a method, a computer program and a computer-readable medium for identifying solar cell defects by deep learning in a solar module image, in particular to the defect identification of monocrystalline silicon solar cells.

中華民國發明專利I653605號揭露一種『利用深度學習的自動光學檢測方法、設備、電腦程式、電腦可讀取之記錄媒體及其深度學習系統』。主要係提供成對影像組合,其中該成對影像組合包括至少一無瑕影像與至少一對應於該無瑕影像的瑕疵影像;提供一卷積神經網路架構,並於該卷積神經網路架構中啟動訓練模式;將複數個該成對影像組合輸入至該卷積神經網路架構,並經由反向傳播演算法調整全連結層個別的權重,以完成訓練;以及利用已訓練的該卷積神經網路架構,執行一光學檢測程序。該案需要使用無瑕影像(goldensample)與瑕疵影像進行人工智慧學習,人工智慧判別時是以整張影像為單位進行判別,有時單一的或微小的瑕疵不易判別出。 The Republic of China Invention Patent No. I653605 discloses an "automatic optical detection method, equipment, computer program, computer-readable recording medium and deep learning system using deep learning". It mainly provides a paired image combination, wherein the paired image combination includes at least one flawless image and at least one flawed image corresponding to the flawless image; a convolutional neural network architecture is provided, and in the convolutional neural network architecture Start the training mode; input a plurality of the paired image combinations to the convolutional neural network structure, and adjust the individual weights of the fully connected layers through a back-propagation algorithm to complete the training; and use the trained convolutional neural network The network architecture executes an optical detection procedure. This case requires the use of flawless images (golden samples) and flawed images for artificial intelligence learning. The artificial intelligence uses the entire image as a unit to discriminate, and sometimes it is difficult to discriminate single or tiny flaws.

中華民國發明專利I701638號揭露一種『應用機械學習技術於自動化光學檢測系統』,包含:一定位裝置,其係用以定位一工件;一影像擷取單元,其係用以擷取該工件之影像;一處理單元,其訊號連結於該影像擷取單元,並係用以執行一尺寸檢測步驟及一光澤檢測步驟,該尺寸檢測步驟包含:將工件之影像銳化,並藉由邊緣檢測以獲得一邊緣影像;將邊緣影像進行卷積運算,並將該卷積影像依軸向計算點之數量;界定點數量變化較高之部分為一邊緣,並計算該邊緣之像素數;透過設定閥值,以藉由像素數與閥值之比對判斷該工件之尺寸是否合格;並可藉由CNN卷積類神經網路訓練工件之合格影像及瑕疵影像,以於拍攝工件影像時,直接進行合格與否之判別;藉此,可自動化進行工件之品質檢測,並可將合格品與不合格品予以分類,以降低人力檢測之成本,並可確保工件生產銷售之品質。該案需要使用定位裝置將工件確實定位,以進行尺寸檢測;且該案仍然是合格影像(goldensample)及瑕疵影像進行學習,且判別時是以整張影像為單位進行判別,有時單一的或微小的瑕疵不易判別出。 The Republic of China Invention Patent No. I701638 discloses an "application of machine learning technology in an automated optical inspection system", comprising: a positioning device for positioning a workpiece; an image capture unit for capturing an image of the workpiece ; a processing unit, the signal of which is connected to the image capture unit, and is used to execute a size detection step and a gloss detection step, the size detection step includes: sharpening the image of the workpiece, and obtaining by edge detection an edge image; perform a convolution operation on the edge image, and calculate the number of points along the axis of the convolved image; define the part with a high change in the number of points as an edge, and calculate the number of pixels on the edge; set a threshold by setting a threshold , to judge whether the size of the workpiece is qualified by the comparison between the number of pixels and the threshold value; the qualified image and the defective image of the workpiece can be trained by the CNN convolutional neural network, so that the qualified image can be directly checked when shooting the workpiece image. Distinguish whether it is or not; in this way, the quality inspection of the workpiece can be automated, and the qualified and unqualified products can be classified, so as to reduce the cost of manual inspection and ensure the quality of the workpiece production and sales. In this case, it is necessary to use a positioning device to accurately position the workpiece for size detection; in this case, the learning is still conducted on the qualified image (golden sample) and the defective image, and the judgment is based on the whole image, sometimes a single or Small flaws are not easy to spot.

中華民國發明專利公開第202001798號揭露一種『光學檢測方法、光學檢測裝置及光學檢測系統』。係經由光學鏡頭擷取待測物的第一影像;對所述第一影像執行邊緣偵測,以獲得具有邊緣圖案的第二影像;以及基於神經網路架構對所述第二影像執行瑕疵檢測操作,以檢測所述第二影像中的瑕疵圖案,其中,神經網路架構的檢測係根據樣本影像產生第一無瑕影像與第一瑕疵影像,並以第一無瑕影像與第一瑕疵影像進行訓練。該案仍然是第一無瑕影像(golden sample)及第一瑕疵影像進行學習,且判別時是以整張影像為單位進行判別,有時單一的或微小的瑕疵不易判別出。 The Republic of China Invention Patent Publication No. 202001798 discloses an "optical detection method, optical detection device and optical detection system". Capture a first image of the object to be tested through an optical lens; perform edge detection on the first image to obtain a second image with an edge pattern; and perform flaw detection on the second image based on a neural network architecture The operation is to detect the flaw pattern in the second image, wherein the detection of the neural network architecture generates a first flawless image and a first flawed image according to the sample image, and performs training on the first flawless image and the first flawed image . In this case, the first flawless image (golden sample) and the first flawed image are still used for learning, and the whole image is used as a unit for determination. Sometimes it is difficult to identify a single or small flaw.

林敬祐等人所發表的『太陽能模組EL瑕疵檢測』技術,主要揭露如何運用影像處理技術快速定位與分割太陽能模組(Solar Module)上單一太陽能板,並透過統計分析與太陽能模組特性建構出標準比對樣本,與測試模組比對找出瑕疵,最後利用瑕疵特性找出太陽能板缺陷。該案根據太陽能板排列而在太陽能模組影像中取出太陽能板之細胞影像,因此細胞影像的邊界參差不齊,雖然該案使用平均灰階值及灰階值標準差作為特徵值進行細胞影像瑕疵辨識,但是仍然需要使用標準樣板(golden sample)來取得上述特徵值,且參差不齊的細胞影像邊界將造成系統計算量過大。 The technology of "Solar Module EL Defect Detection" published by Lin Jingyou et al. mainly discloses how to use image processing technology to quickly locate and segment a single solar panel on a solar module, and construct through statistical analysis and solar module characteristics A standard comparison sample is produced, compared with the test module to find out the defects, and finally the defects of the solar panel are found out by using the defect characteristics. In this case, the cell image of the solar panel is extracted from the solar module image according to the arrangement of the solar panel, so the boundary of the cell image is uneven, although this case uses the average gray level value and the standard deviation of the gray level value as the feature values for cell image defects However, it is still necessary to use a standard sample (golden sample) to obtain the above-mentioned eigenvalues, and the uneven cell image boundary will cause the system to calculate too much.

爰此,本發明提出一種在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,包括下列步驟:對一太陽能模組執行一電致發光程序,並對該太陽能模組擷取一模組影像。對該模組影像執行一邊界修正程序。對該模組影像執行一影像分割程序,獲得複數細胞格影像。對上述細胞格影像執行一人工智慧辨識程序,該人工智慧辨識程序根據一影像特徵值將複數細胞格影像區分為一瑕疵細胞格影像或一非瑕疵細胞格影像,藉此篩選出該太陽能模組上具有瑕疵的一太陽能電池。 Therefore, the present invention provides a method for identifying solar cell defects in a solar module image by deep learning, including the following steps: performing an electroluminescence process on a solar module, and capturing a pattern of the solar module. group images. A boundary correction procedure is performed on the module image. An image segmentation procedure is performed on the module image to obtain a plurality of cell grid images. Performing an artificial intelligence identification program on the above cell image, the artificial intelligence identification program distinguishes a plurality of cell images into a defective cell image or a non-defective cell image according to an image feature value, thereby screening out the solar module A solar cell with defects on it.

進一步,預先以人為方式篩選出該影像瑕疵特徵值,並經由人工智慧深度學習後,對上述細胞格影像自動辨識。 Further, the feature values of the image flaws are manually screened in advance, and after deep learning by artificial intelligence, the above-mentioned cell image is automatically identified.

進一步,上述影像特徵值包含裂痕影像特徵值、髒污影像特徵值、黑點影像特徵值、斷線影像特徵值及平滑度影像特徵值之一或組合。 Further, the above-mentioned image feature values include one or a combination of crack image feature values, dirty image feature values, black dot image feature values, broken line image feature values, and smoothness image feature values.

進一步,所述人工智慧深度學習及人工智慧辨識程序係採用類神經網絡。 Further, the artificial intelligence deep learning and artificial intelligence identification programs use neural networks.

進一步,該邊界修正程序係根據灰階值的變化取得。 Further, the boundary correction program is obtained according to the change of the gray scale value.

進一步,該影像分割程序係根據霍夫轉換取得複數縱橫軸線之複數交點,並根據連接上述交點而分割出上述細胞格影像。更進一步,根據該太陽能模組的該太陽能電池的排列決定該模組影像的複數交點區域,並將位在上述交點區域的複數交點取座標平均值,分別獲得上述交點區域的一分割交點,並連接上述分割交點而分割出上述細胞格影像,且分割出的每一細胞格影像為單一完整的一太陽能電池影像。 Further, the image segmentation program obtains the complex intersection points of the complex vertical and horizontal axes according to the Hough transform, and divides the cell image according to the connection of the intersection points. Further, according to the arrangement of the solar cells of the solar module, the complex intersection area of the module image is determined, and the coordinate average of the complex intersection points located in the intersection area is taken to obtain a divided intersection of the intersection area respectively, and The cell grid images are segmented by connecting the segmentation intersection points, and each segmented cell grid image is a single complete solar cell image.

本發明再提出一種電腦程式,供使用於一電腦,該電腦程式執行如前述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法。 The present invention further provides a computer program for use in a computer, and the computer program executes the aforementioned method for identifying solar cell defects in solar module images by deep learning.

本發明再提出一種電腦可讀取媒體,儲存有一電腦程式,該電腦程式執行如前述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法。 The present invention further provides a computer-readable medium storing a computer program, and the computer program executes the method for identifying solar cell defects by deep learning in a solar module image as described above.

根據上述技術特徵可達成以下功效: According to the above technical features, the following effects can be achieved:

1.本發明提供一種細胞格影像切割結合深度學習的計算架構,使太陽能電池的瑕疵篩選更為快速精確。 1. The present invention provides a computing architecture combining cell grid image cutting and deep learning, which makes the defect screening of solar cells more rapid and accurate.

2.不需要成對的瑕疵原始影像(golden sample)與無瑕疵原始影像,即可執行太陽能電池的瑕疵篩選。 2. Defect screening of solar cells can be performed without a pair of defective original images (golden samples) and non-defective original images.

3.本發明採用的細胞格影像分割,不需要精準的定位裝置搭配。 3. The cell grid image segmentation adopted in the present invention does not require precise positioning device matching.

4.本發明提出的邊界修正程序,可以處理不定尺寸的細胞格影像切割。 4. The boundary correction program proposed by the present invention can process image cutting of cell grids of indeterminate size.

5.本發明細胞格影像分割方法,為全域最顯著分割的計算模式,本發明的分割結果,不會產生細胞格影像的邊界之間不連續的斷節。 5. The cell grid image segmentation method of the present invention is the calculation mode for the most significant segmentation in the whole domain, and the segmentation result of the present invention does not produce discontinuous segments between the boundaries of the cell grid images.

6.本發明深度學習的計算模式,可以容忍細胞格影像切割不精確的問題。 6. The deep learning computing mode of the present invention can tolerate the problem of inaccurate cell image cutting.

1:模組影像 1: Module image

11:邊框影像 11: Border image

12:縱橫軸線 12: Vertical and horizontal axis

121:縱軸線 121: longitudinal axis

122:橫軸線 122: Transverse axis

13:交點 13: Intersection

14:交點區域 14: Intersection area

15:分割交點 15: Split Intersection

2:細胞格影像 2: Cell grid image

21:瑕疵細胞格影像 21: Flawed cell grid image

22:非瑕疵細胞格影像 22: Unblemished cell grid image

3:影像特徵值 3: Image eigenvalues

[第一圖]為本發明實施例的流程圖。 [Figure 1] is a flowchart of an embodiment of the present invention.

[第二圖]為本發明實施例中,擷取太陽能模組之模組影像的示意圖。 [Fig. 2] is a schematic diagram of capturing a module image of a solar module in an embodiment of the present invention.

[第三圖]為本發明實施例中,對太陽能模組之模組影像進行邊界修正程序的示意圖。 [Figure 3] is a schematic diagram of a process of performing boundary correction on a module image of a solar module according to an embodiment of the present invention.

[第四圖]為本發明實施例中,利用霍夫轉換取得模組影像中的交點的示意圖。 [FIG. 4] is a schematic diagram of obtaining intersection points in a module image by using Hough transform in an embodiment of the present invention.

[第五圖]為本發明實施例中,在模組影像的交點區域的複數交點中取得分割交點的示意圖。 [FIG. 5] is a schematic diagram of obtaining segmented intersections from the complex intersections in the intersection area of the module image according to an embodiment of the present invention.

[第六圖]為本發明實施例中,在模組影像中連接分割交點而取得細胞格影像的示意圖。 [Fig. 6] is a schematic diagram of obtaining a cell grid image by connecting segmentation intersections in a module image according to an embodiment of the present invention.

[第七圖]為本發明實施例中,利用人工智慧辨識程序將細胞格影像區隔出瑕疵細胞格影像與非瑕疵細胞格影像的示意圖。 [Fig. 7] is a schematic diagram of using an artificial intelligence recognition program to separate a defective cell grid image and a non-defective cell grid image from a cell grid image according to an embodiment of the present invention.

綜合上述技術特徵,本發明在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法、電腦程式及電腦可讀取媒體的主要功效將可於下述實施例清楚呈現。 In view of the above technical features, the method, computer program and computer-readable medium for identifying solar cell defects in solar module images by deep learning of the present invention will be clearly presented in the following embodiments.

參閱第一圖所示,本實施例係在一電腦安裝一電腦程式以執行在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,該電腦程式 可透過電腦可讀取媒體儲存。所述在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法包括下列步驟: 參閱第一圖至第三圖所示,對一太陽能模組執行一電致發光程序,並對該太陽能模組擷取一模組影像1,該模組影像1以灰階影像為例。通常,該模組影像1會包含有例如一邊框影像11,因此,對該模組影像執行一邊界修正程序,以去除該邊框影像11,本實施例該邊界修正程序係根據灰階值的變化來判別該邊框影像11並加以去除,例如在灰階值的變化大於5%時,設定為邊框影像11。本發明透過執行上述邊界修正程序,該太陽能模組不需要使用精準的定位裝置來進行定位;也透過執行上述邊界修正程序而在後續步驟可以處理不定尺寸的細胞格影像切割。 Referring to the first figure, the present embodiment is to install a computer program on a computer to execute a method for identifying solar cell defects by deep learning in solar module images. The computer program Computer-readable media storage. The method for identifying solar cell defects in solar module images by deep learning includes the following steps: Referring to Figures 1 to 3, an electroluminescence process is performed on a solar module, and a module image 1 is captured from the solar module. The module image 1 is a grayscale image as an example. Usually, the module image 1 includes, for example, a frame image 11. Therefore, a boundary correction procedure is performed on the module image to remove the frame image 11. In this embodiment, the boundary correction procedure is based on the change of the grayscale value. The frame image 11 is determined and removed. For example, when the change of the gray level value is greater than 5%, the frame image 11 is set. In the present invention, by executing the above-mentioned boundary correction procedure, the solar module does not need to use an accurate positioning device for positioning; and by executing the above-mentioned boundary correction procedure, the cell grid image cutting of indeterminate size can be processed in subsequent steps.

參閱第一圖、第四圖至第六圖所示,對該模組影像1執行一影像分割程序,以獲得複數細胞格影像2,該影像分割程序係根據霍夫轉換在該模組影像1上取得複數縱橫軸線12之複數交點13,其中霍夫轉換可參閱維基百科所述『霍夫轉換是一種特徵提取,被廣泛應用在圖像分析、電腦視覺以及數位影像處理。霍夫轉換是用來辨別找出物件中的特徵,例如:線條。他的演算法流程大致如下,給定一個物件、要辨別的形狀的種類,演算法會在參數空間中執行投票來決定物體的形狀,而這是由累加空間(accumulator space)裡的局部最大值來決定。』因此霍夫轉換演算法為習知技術,在此不贅述。之後根據連接上述交點13而分割出上述細胞格影像2,詳細而言,係根據該太陽能模組的該太陽能電池的排列決定該模組影像1的複數交點區域14,例如本實施例之太陽能模組的該太陽能電池排列為10*6之矩陣排列,而在縱軸線121上有7個交點區域,而在橫軸線122上有11個交點區域14,將位在上述交點區域14的複數交點13取座標平均值, 分別獲得上述交點區域14的一分割交點15。並連接上述分割交點15而分割出上述細胞格影像2,且分割出的每一細胞格影像2為單一完整的一太陽能電池影像。透過上述細胞格影像2之分割方法,為全域最顯著分割的計算模式,因此上述細胞格影像2的邊界之間不會產生不連續的斷節。 Referring to Figure 1, Figure 4 to Figure 6, an image segmentation process is performed on the module image 1 to obtain a plurality of cell grid images 2. The image segmentation process is based on the Hough transform in the module image 1. The complex intersection point 13 of the complex vertical and horizontal axes 12 is obtained on the above, and the Hough transform can be referred to in Wikipedia. "The Hough transform is a feature extraction, which is widely used in image analysis, computer vision and digital image processing. Hough transform is used to identify features in objects, such as lines. The flow of his algorithm is roughly as follows. Given an object and the type of shape to be identified, the algorithm will perform a vote in the parameter space to determine the shape of the object, which is determined by the local maximum in the accumulator space (accumulator space) to decide. Therefore, the Hough transform algorithm is a conventional technique, and details are not described here. Then, the cell image 2 is segmented according to the connection of the intersections 13 . Specifically, the plurality of intersection regions 14 of the module image 1 are determined according to the arrangement of the solar cells of the solar module, such as the solar module of this embodiment. The solar cells of the group are arranged in a 10*6 matrix, and there are 7 intersection areas on the longitudinal axis 121 and 11 intersection areas 14 on the horizontal axis 122, which will be located in the above-mentioned intersection areas 14. The complex intersection points 13 Take the average of the coordinates, A segmented intersection 15 of the above-mentioned intersection area 14 is obtained, respectively. The cell image 2 is segmented by connecting the segment intersection 15 , and each segmented cell image 2 is a single complete solar cell image. The above-mentioned segmentation method of the cell grid image 2 is the calculation mode of the most significant segmentation in the whole area, so that discontinuous segments will not be generated between the boundaries of the above-mentioned cell grid image 2 .

參閱第七圖所示,對上述細胞格影像2執行一人工智慧辨識程序,該人工智慧辨識程序根據一影像特徵值3將複數細胞格影像2區分為一瑕疵細胞格影像21或一非瑕疵細胞格影像22,藉此篩選出該太陽能模組上具有瑕疵的一太陽能電池。其中,上述影像特徵值3係預先以人為方式篩選出來,並經由人工智慧深度學習後,用以對上述細胞格影像2自動辨識,因此不需要如習知辨識方式需要無瑕疵影像(golden sample)進行比對。上述影像特徵值3可包含裂痕影像特徵值、髒污影像特徵值、黑點影像特徵值、斷線影像特徵值及平滑度影像特徵值之一或組合,而所述人工智慧深度學習及人工智慧辨識程序例如可採用類神經網絡,此人工智慧演算法係為習知技術,在此不贅述。而透過人工智慧深度學習並判別的方式,可以容忍上述細胞格影像2切割不精確的問題。 Referring to Fig. 7, an artificial intelligence recognition program is performed on the cell image 2, and the artificial intelligence recognition program distinguishes the plurality of cell images 2 into a defective cell image 21 or a non-defective cell according to an image feature value 3 The grid image 22 is screened out to screen out a solar cell with defects on the solar module. Wherein, the above-mentioned image feature value 3 is pre-screened by humans, and after deep learning by artificial intelligence, it is used to automatically identify the above-mentioned cell image 2, so there is no need for a flawless image (golden sample) as in the conventional identification method. Compare. The above-mentioned image feature value 3 may include one or a combination of crack image feature value, dirty image feature value, black point image feature value, broken line image feature value, and smoothness image feature value, and the artificial intelligence deep learning and artificial intelligence For example, the identification procedure can use a neural network-like algorithm, and the artificial intelligence algorithm is a conventional technology, and will not be repeated here. Through the deep learning and discrimination method of artificial intelligence, the problem of inaccurate cutting of the above-mentioned cell grid image 2 can be tolerated.

參閱下表所示,將人工智慧深度學習後之辨識模型統計其訓練後之正確率、損失率及交叉驗證後之正確率、損失率,可發現其正確率之平均均為0.9777以上,且損失率低於0.0748以下,且代表變異度的標準差在0.0089以下,顯示了本發明檢測的有效性。 Referring to the table below, the accuracy rate and loss rate after training and the accuracy rate and loss rate after cross-validation of the recognition model after artificial intelligence deep learning are counted. It can be found that the average accuracy rate is above 0.9777, and the loss rate The rate is below 0.0748, and the standard deviation representing the degree of variation is below 0.0089, which shows the validity of the detection of the present invention.

Figure 109140882-A0305-02-0009-1
Figure 109140882-A0305-02-0009-1

Figure 109140882-A0305-02-0010-2
Figure 109140882-A0305-02-0010-2

綜合上述實施例之說明,當可充分瞭解本發明之操作、使用及本發明產生之功效,惟以上所述實施例僅係為本發明之較佳實施例,當不能以此限定本發明實施之範圍,即依本發明申請專利範圍及發明說明內容所作簡單的等效變化與修飾,皆屬本發明涵蓋之範圍內。 Based on the descriptions of the above embodiments, one can fully understand the operation, use and effects of the present invention, but the above-mentioned embodiments are only preferred embodiments of the present invention, which should not limit the implementation of the present invention. Scope, that is, simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the description of the invention, all fall within the scope of the present invention.

Claims (8)

一種在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,包含下列步驟:對一太陽能模組執行一電致發光程序,並對該太陽能模組擷取一模組影像;對該模組影像執行一邊界修正程序;對該模組影像執行一影像分割程序,獲得複數細胞格影像;對上述細胞格影像執行一人工智慧辨識程序,該人工智慧辨識程序根據一影像特徵值將複數細胞格影像區分為一瑕疵細胞格影像或一非瑕疵細胞格影像,藉此篩選出該太陽能模組上具有瑕疵的一太陽能電池;其中,該影像分割程序係根據霍夫轉換取得複數縱橫軸線之複數交點,並根據連接上述交點而分割出上述細胞格影像,且分割出的每一細胞格影像為單一完整的一太陽能電池影像。 A method for identifying solar cell defects by deep learning in solar module images, comprising the following steps: performing an electroluminescence process on a solar module, and capturing a module image for the solar module; A boundary correction program is performed on the group of images; an image segmentation program is performed on the module image to obtain a plurality of cell grid images; an artificial intelligence recognition program is performed on the above-mentioned cell grid images, and the artificial intelligence recognition program divides the plurality of cells according to an image feature value. The grid image is divided into a defective cell grid image or a non-defective cell grid image, thereby screening out a solar cell with defects on the solar module; wherein, the image segmentation process obtains the complex number of the vertical and horizontal axes according to the Hough transformation The intersection points are connected, and the cell grid images are segmented according to the connection of the intersection points, and each segmented cell grid image is a single complete solar cell image. 如請求項1所述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,其中,預先以人為方式篩選出該影像瑕疵特徵值,並經由人工智慧深度學習後,對上述細胞格影像自動辨識。 The method for identifying solar cell defects in solar module images using deep learning as described in claim 1, wherein the image defect feature values are screened out in advance, and after deep learning by artificial intelligence, the above cell grids are Image recognition automatically. 如請求項2所述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,其中,上述影像特徵值包含裂痕影像特徵值、髒污影像特徵值、黑點影像特徵值、斷線影像特徵值及平滑度影像特徵值之一或組合。 The method for identifying solar cell defects in solar module images by deep learning according to claim 2, wherein the image feature values include crack image feature values, dirty image feature values, black dot image feature values, and broken lines. One or a combination of image eigenvalues and smoothness image eigenvalues. 如請求項3所述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,其中,所述人工智慧深度學習及人工智慧辨識程序係採用類神經網絡。 The method for identifying solar cell defects in solar module images by deep learning according to claim 3, wherein the artificial intelligence deep learning and artificial intelligence identification procedures use neural networks. 如請求項1所述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,其中,該邊界修正程序係根據灰階值的變化取得。 The method for identifying solar cell defects in solar module images using deep learning as claimed in claim 1, wherein the boundary correction procedure is obtained according to changes in grayscale values. 如請求項1所述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法,其中,根據該太陽能模組的該太陽能電池的排列決定該模組影像的複數交點區域,並將位在上述交點區域的複數交點取座標平均值,分別獲得上述交點區域的一分割交點,並連接上述分割交點而分割出上述細胞格影像。 The method for identifying solar cell defects in a solar module image by deep learning as described in claim 1, wherein the complex intersection area of the module image is determined according to the arrangement of the solar cells of the solar module, and the position The coordinates are averaged at the complex intersections in the intersection area, and a segmented intersection of the intersection area is obtained, and the divided intersections are connected to segment the cell image. 一種電腦程式,供使用於一電腦,該電腦程式執行如請求項1至請求項6中任一項所述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法。 A computer program for use in a computer, the computer program executing the method for identifying solar cell defects with deep learning in solar module images as described in any one of claim 1 to claim 6. 一種電腦可讀取媒體,儲存有一電腦程式,該電腦程式執行如請求項1至請求項6中任一項所述之在太陽能模組影像中以深度學習進行太陽能電池瑕疵辨識的方法。 A computer-readable medium stores a computer program, the computer program executes the method for identifying solar cell defects by deep learning in solar module images as described in any one of claim 1 to claim 6.
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