Optics and Precision Engineering, Volume. 32, Issue 6, 868(2024)

Review of defect detection algorithms for solar cells based on machine vision

Yuqi LIU and Yiquan WU*
Author Affiliations
  • College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing211106, China
  • show less
    References(129)

    [1] KABIR E, KUMAR P, KUMAR S et al. Solar energy: potential and future prospects[J]. Renewable and Sustainable Energy Reviews, 82, 894-900(2018).

    [2] ŠKARVADA P, TOMÁNEK P, KOKTAVÝ P et al. A variety of microstructural defects in crystalline silicon solar cells[J]. Applied Surface Science, 312, 50-56(2014).

    [3] DHIMISH M, MATHER P. Development of novel solar cell micro crack detection technique[J]. IEEE Transactions on Semiconductor Manufacturing, 32, 277-285(2019).

    [4] [4] 周求湛, 张彦创, 周承鹏, 等. 1/f噪声的精确测量及其在太阳能电池可靠性筛选中的应用[J]. 光学 精密工程, 2012, 20(3): 625-631. doi: 10.3788/ope.20122003.0625ZHOUQ Z, ZHANGY C, ZHOUC P, et al. Precise measurement of 1/f noise and its application to reliability screening for solar cells[J]. Opt. Precision Eng., 2012, 20(3): 625-631.(in Chinese). doi: 10.3788/ope.20122003.0625

    [5] HILMERSSON C, HESS D P, DALLAS W et al. Crack detection in single-crystalline silicon wafers using impact testing[J]. Applied Acoustics, 69, 755-760(2008).

    [6] LI B, HE X H, FANG S. Automatic inspection of surface crack in solar cell images[C], 993-998(2011).

    [7] [7] 周健, 卞洁玉, 李红飞, 等. 晶体硅光伏电池的标准测试[J]. 光学 精密工程, 2014, 22(6): 1517. doi: 10.3788/ope.20142206.1517ZHOUJ, BIANJ Y, LIH F, et al. Standard measurement of crystal silicon solar cells[J]. Opt. Precision Eng., 2014, 22(6): 1517.(in Chinese). doi: 10.3788/ope.20142206.1517

    [8] KUMAR S, JENA P, SINHA A et al. Application of infrared thermography for non-destructive inspection of solar photovoltaic module[J]. Nondestructive Testing and Evaluation, 6, 25-32(2017).

    [9] GALLARDO-SAAVEDRA S, HERNÁNDEZ-CALLEJO L, DEL CARMEN ALONSO-GARCÍA M et al. Nondestructive characterization of solar PV cells defects by means of electroluminescence, infrared thermography, I–V curves and visual tests: experimental study and comparison[J]. Energy, 205, 117930(2020).

    [10] DU B L, YANG R Z, HE Y Z et al. Nondestructive inspection, testing and evaluation for Si-based, thin film and multi-junction solar cells: an overview[J]. Renewable and Sustainable Energy Reviews, 78, 1117-1151(2017).

    [11] YIN C C, WEN T K. ESPI solution for defect detection in crystalline photovoltaic cells[C], 832-837(2011).

    [12] [12] 刘剑, 陆建, 倪晓武, 等. 单晶硅片在脉冲激光作用下的断裂行为[J]. 光学 精密工程, 2011, 19(2): 414-420. doi: 10.3788/ope.20111902.0414LIUJ, LUJ, NIX W, et al. Fracture behavior during pulsed laser irradiating silicon wafer[J]. Opt. Precision Eng., 2011, 19(2): 414-420.(in Chinese). doi: 10.3788/ope.20111902.0414

    [13] [13] 陈文志, 张凤燕, 张然, 等. 基于电致发光成像的太阳能电池缺陷检测[J]. 发光学报, 2013, 34(8): 1028-1034. doi: 10.3788/fgxb20133408.1028CHENW Z, ZHANGF Y, ZHANGR, et al. Defect detection of solar cells based on electroluminescence imaging[J]. Chinese Journal of Luminescence, 2013, 34(8): 1028-1034.(in Chinese). doi: 10.3788/fgxb20133408.1028

    [14] DRABCZYK K, KULESZA-MATLAK G, DRYGA\LA A et al. Electroluminescence imaging for determining the influence of metallization parameters for solar cell metal contacts[J]. Solar Energy, 126, 14-21(2016).

    [15] QI S X, YANG J R, ZHONG Z Y. A review on industrial surface defect detection based on deep learning technology[C], 24-30(2020).

    [16] [16] 赵朗月, 吴一全. 基于机器视觉的表面缺陷检测方法研究进展[J]. 仪器仪表学报, 2022, 43(1): 198-219.ZHAOL Y, WUY Q. Research progress of surface defect detection methods based on machine vision[J]. Chinese Journal of Scientific Instrument, 2022, 43(1): 198-219.(in Chinese)

    [17] [17] 王宪保, 李洁, 姚明海, 等. 基于深度学习的太阳能电池片表面缺陷检测方法[J]. 模式识别与人工智能, 2014, 27(6): 517-523. doi: 10.3969/j.issn.1003-6059.2014.06.006WANGX B, LIJ, YAOM H, et al. Solar cells surface defects detection based on deep learning[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(6): 517-523.(in Chinese). doi: 10.3969/j.issn.1003-6059.2014.06.006

    [18] ABDELHAMID M, SINGH R, OMAR M. Review of microcrack detection techniques for silicon solar cells[J]. IEEE Journal of Photovoltaics, 4, 514-524(2014).

    [19] ISRAIL M, ANWAR S A, ABDULLAH M Z. Automatic detection of micro-crack in solar wafers and cells: a review[J]. Transactions of the Institute of Measurement and Control, 35, 606-618(2013).

    [20] ENNEMRI A, LOGERAIS P O, BALISTROU M et al. Cracks in silicon photovoltaic modules: a review[J]. Journal of Optoelectronics and Advanced Materials, 21, 74-92(2019).

    [21] [21] 钱晓亮, 张鹤庆, 陈永信, 等. 基于机器视觉的太阳能电池片表面缺陷检测研究现状及展望[J]. 北京工业大学学报, 2017, 43(1): 76-85. doi: 10.11936/bjutxb2016040063QIANX L, ZHANGH Q, CHENY X, et al. Research development and prospect of solar cells surface defects detection based on machine vision[J]. Journal of Beijing University of Technology, 2017, 43(1): 76-85.(in Chinese). doi: 10.11936/bjutxb2016040063

    [22] QIAN X L, ZHANG H Q, ZHANG H L et al. Solar cell surface defects detection based on computer vision[J]. International Journal of Performability Engineering, 13, 1048-1056(2017).

    [23] KANDEAL A W, ELKADEEM M R, KUMAR THAKUR A et al. Infrared thermography-based condition monitoring of solar photovoltaic systems: a mini review of recent advances[J]. Solar Energy, 223, 33-43(2021).

    [24] ANWAR S A, ABDULLAH M Z. Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique[J]. EURASIP Journal on Image and Video Processing, 2014, 15(2014).

    [25] TSAI D M, LUO J Y. Mean shift-based defect detection in multicrystalline solar wafer surfaces[J]. IEEE Transactions on Industrial Informatics, 7, 125-135(2011).

    [26] TSAI D M, CHANG C C, CHAO S M. Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion[J]. Image and Vision Computing, 28, 491-501(2010).

    [27] RHEEM J. Anisotropic diffusion based micro-crack inspection in polycrystalline solar wafers[C], 2188, 524-528(2010).

    [28] RHEEM J. Micro-crack detection in polycrystalline solar cells using improved anisotropic diffusion model[J]. Journal of the Institute of Electronics Engineers of Korea, 50, 183-190(2013).

    [29] AGHAMOHAMMADI A H, PRABUWONO A S, SAHRAN S et al. Solar cell panel crack detection using particle swarm optimization algorithm[C], 160-164(2011).

    [30] DAFNY LYDIA M D, SINDHU K S, GUGAN K. Analysis on solar panel crack detection using optimization techniques[J]. Journal of Nano-and Electronic Physics, 9, -5(2017).

    [31] STROMER D, VETTER A, OEZKAN H C et al. Enhanced crack segmentation (eCS): a reference algorithm for segmenting cracks in multicrystalline silicon solar cells[J]. IEEE Journal of Photovoltaics, 9, 752-758(2019).

    [32] [32] 李洁, 袁知博, 秦嘉悦. 基于Sobel算子边缘检测的太阳电池缺陷特征提取方法[J]. 太阳能学报, 2021, 42(1): 63-68.LIJ, YUANZ B, QINJ Y. Research on solar cells defects feature extraction based on sobel operator edge detection[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 63-68.(in Chinese)

    [33] DEMANT M, WELSCHEHOLD T, OSWALD M et al. Microcracks in silicon wafers I: inline detection and implications of crack morphology on wafer strength[J]. IEEE Journal of Photovoltaics, 6, 126-135(2016).

    [34] [34] 刘磊, 王冲, 赵树旺, 等. 基于机器视觉的太阳能电池片缺陷检测技术的研究[J]. 电子测量与仪器学报, 2018, 32(10): 47-52. doi: 10.13382/j.jemi.2018.10.007LIUL, WANGC, ZHAOS W, et al. Research on solar cells defect detection technology based on machine vision[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(10): 47-52.(in Chinese). doi: 10.13382/j.jemi.2018.10.007

    [35] DEITSCH S, CHRISTLEIN V, BERGER S et al. Automatic classification of defective photovoltaic module cells in electroluminescence images[J]. Solar Energy, 185, 455-468(2019).

    [36] ALCANTARILLA P F, BARTOLI A, DAVISON A J[M]. KAZE Features, 214-227(2012).

    [37] LOWE D G. Object recognition from local scale-invariant features[C], 1150-1157(27).

    [38] TUYTELAARS T, VAN GOOL L[M]. SURF: Speeded up Robust Features, 404-417(2006).

    [39] SERFA JUAN R O, KIM J. Photovoltaic cell defect detection model based-on extracted electroluminescence images using SVM classifier[C], 578-582(2020).

    [40] [40] 伍李春, 刘明周, 蒋倩男, 等. 基于人工神经网络的太阳能电池片表面质量检测系统[J]. 合肥工业大学学报(自然科学版), 2017, 40(9): 1176-1180, 1192.WUL C, LIUM Z, JIANGQ N, et al. Solar cell surface quality detection system based on artificial neural network[J]. Journal of Hefei University of Technology (Natural Science), 2017, 40(9): 1176-1180, 1192.(in Chinese)

    [41] DEMIRCI MY, BEŞLI N, GÜMÜŞÇÜ A. Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in electroluminescence images[J]. Expert Systems With Applications, 175, 114810(2021).

    [42] [42] 张玮华. 太阳能电池硅片缺陷自动检测分类方法研究[D]. 上海: 东华大学, 2014.ZHANGW H. Research on Method of Solar Cell Silicon Defects Automatic Detection Classification[D]. Shanghai: Donghua University, 2014. (in Chinese)

    [43] FU Z, ZHAO Y Z, LIU Y et al. Solar cell crack inspection by image processing[C], 77-80(2004).

    [44] TSENG D C, LIU Y S, CHOU C M. Automatic finger interruption detection in electroluminescence images of multicrystalline solar cells[J]. Mathematical Problems in Engineering, 2015, 879675(2015).

    [45] TSAI D M, LI G N, LI W C et al. Defect detection in multi-crystal solar cells using clustering with uniformity measures[J]. Advanced Engineering Informatics, 29, 419-430(2015).

    [46] XU P, ZHOU W J, FEI M R. Detection methods for micro-cracked defects of photovoltaic modules based on machine vision[C], 609-613(2014).

    [47] SU B Y, CHEN H Y, ZHU Y F et al. Classification of manufacturing defects in multicrystalline solar cells with novel feature descriptor[J]. IEEE Transactions on Instrumentation and Measurement, 68, 4675-4688(2019).

    [48] SONG X Y, YANG L, XIA X H et al. Defect detection method for solar cells based on human visual characteristics[C], 515-518(2020).

    [49] CHIOU Y C, LIU J Z, LIANG Y T. Micro crack detection of multi-crystalline silicon solar wafer using machine vision techniques[J]. Sensor Review, 31, 154-165(2011).

    [50] [50] 姚明海, 李洁, 王宪保. 基于RPCA的太阳能电池片表面缺陷检测[J]. 计算机学报, 2013, 36(9): 1943-1952. doi: 10.3724/sp.j.1016.2013.01943YAOM H, LIJ, WANGX B. Solar cells surface defects detection using RPCA method[J]. Chinese Journal of Computers, 2013, 36(9): 1943-1952.(in Chinese). doi: 10.3724/sp.j.1016.2013.01943

    [51] [51] 钱晓亮, 张鹤庆, 张焕龙, 等. 基于视觉显著性的太阳能电池片表面缺陷检测[J]. 仪器仪表学报, 2017, 38(7): 1570-1578. doi: 10.3969/j.issn.0254-3087.2017.07.002QIANX L, ZHANGH Q, ZHANGH L, et al. Solar cell surface defect detection based on visual saliency[J]. Chinese Journal of Scientific Instrument, 2017, 38(7): 1570-1578.(in Chinese). doi: 10.3969/j.issn.0254-3087.2017.07.002

    [52] [52] 陶志勇, 于子佳, 林森. PSOSVM算法在太阳能电池板裂缝缺陷检测研究[J]. 电子测量与仪器学报, 2021, 35(1): 18-25.TAOZ Y, YUZ J, LINS. Research on crack defect detection of solar cell based on PSOSVM[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(1): 18-25.(in Chinese)

    [53] LI W C, TSAI D M. Automatic saw-mark detection in multicrystalline solar wafer images[J]. Solar Energy Materials and Solar Cells, 95, 2206-2220(2011).

    [54] TSAI D M, WU S C, LI W C. Defect detection of solar cells in electroluminescence images using Fourier image reconstruction[J]. Solar Energy Materials and Solar Cells, 99, 250-262(2012).

    [55] DHIMISH M, HOLMES V. Solar cells micro crack detection technique using state-of-the-art electroluminescence imaging[J]. Journal of Science: Advanced Materials and Devices, 4, 499-508(2019).

    [56] LI W C, TSAI D M. Wavelet-based defect detection in solar wafer images with inhomogeneous texture[J]. Pattern Recognition, 45, 742-756(2012).

    [57] [57] 陈海永, 余力, 刘辉, 等. 基于经验小波的太阳能电池缺陷图像融合[J]. 山东大学学报(工学版), 2018, 48(5): 24-31.CHENH Y, YUL, LIUH, et al. Solar cell defect images fusion based on empirical wavelet[J]. Journal of Shandong University (Engineering Science), 2018, 48(5): 24-31.(in Chinese)

    [58] TSAI D M, WU S C, CHIU W Y. Defect detection in solar modules using ICA basis images[J]. IEEE Transactions on Industrial Informatics, 9, 122-131(2013).

    [59] [59] 龚芳, 张学武, 孙浩. 基于独立分量分析和粒子群算法的太阳能电池表面缺陷红外热成像检测[J]. 光学学报, 2012, 32(4): 169-177. doi: 10.3788/aos201232.0415002GONGF, ZHANGX W, SUNH. Detection system for solar module surface defects based on constrained ICA model and PSO method[J]. Acta Optica Sinica, 2012, 32(4): 169-177.(in Chinese). doi: 10.3788/aos201232.0415002

    [60] QIAN X L, ZHANG H Q, YANG C X et al. Micro-cracks detection of multicrystalline solar cell surface based on self-learning features and low-rank matrix recovery[J]. Sensor Review, 38, 360-368(2018).

    [61] AKRAM M W, LI G Q, JIN Y et al. Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning[J]. Solar Energy, 198, 175-186(2020).

    [62] [62] 吴涛, 赖菲. 基于LeNet-5模型的太阳能电池板缺陷识别分类[J]. 热力发电, 2019, 48(3): 120-125. doi: 10.19666/j.rlfd.201807147WUT, LAIF. Identification and classification of defects in solar cells based on LeNet-5 model[J]. Thermal Power Generation, 2019, 48(3): 120-125.(in Chinese). doi: 10.19666/j.rlfd.201807147

    [63] CHEN H Y, WANG S, XING J. Detection of cracks in electroluminescence images by fusing deep learning and structural decoupling[C], 2565-2569(2019).

    [64] PIERDICCA R, MALINVERNI E S, PICCININI F et al. Deep convolutional neural network for automatic detection of damaged photovoltaic cells[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 422, 893-900(2018).

    [65] BARTLER A, MAUCH L, YANG B et al. Automated detection of solar cell defects with deep learning[C], 2035-2039(2018).

    [66] AKRAM M W, LI G Q, JIN Y et al. CNN based automatic detection of photovoltaic cell defects in electroluminescence images[J]. Energy, 189, 116319(2019).

    [67] ZYOUT I, OATAWNEH A. Detection of PV solar panel surface defects using transfer learning of the deep convolutional neural networks[C], 1-4(2020).

    [68] RAHMAN M R, TABASSUM S, HAQUE E et al. CNN-based deep learning approach for micro-crack detection of solar panels[C], 1-6(2021).

    [69] TANG W Q, YANG Q, HU X C et al. Convolution neural network based polycrystalline silicon photovoltaic cell linear defect diagnosis using electroluminescence images[J]. Expert Systems with Applications, 202, 117087(2022).

    [70] HASSAN S, DHIMISH M. Dual spin max pooling convolutional neural network for solar cell crack detection[J]. Scientific Reports, 13, 11099(2023).

    [71] TANG W Q, YANG Q, XIONG K X et al. Deep learning based automatic defect identification of photovoltaic module using electroluminescence images[J]. Solar Energy, 201, 453-460(2020).

    [72] DEMIRCI M Y, BEŞLI N, GÜMÜŞÇÜ A. Defective PV cell detection using deep transfer learning and EL imaging[C](2019).

    [73] CHEN H Y, PANG Y, HU Q D et al. Solar cell surface defect inspection based on multispectral convolutional neural network[J]. Journal of Intelligent Manufacturing, 31, 453-468(2020).

    [74] KORKMAZ D, ACIKGOZ H. An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network[J]. Engineering Applications of Artificial Intelligence, 113, 104959(2022).

    [75] GE C P, LIU Z, FANG L M et al. A hybrid fuzzy convolutional neural network based mechanism for photovoltaic cell defect detection with electroluminescence images[J]. IEEE Transactions on Parallel and Distributed Systems, 32, 1653-1664(2021).

    [76] RICO ESPINOSA A, BRESSAN M, GIRALDO L F. Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks[J]. Renewable Energy, 162, 249-256(2020).

    [77] AHMAD A, JIN Y, ZHU C G et al. Photovoltaic cell defect classification using convolutional neural network and support vector machine[J]. IET Renewable Power Generation, 14, 2693-2702(2020).

    [78] ZHAO X L, SONG C H, ZHANG H F et al. HRNet-based automatic identification of photovoltaic module defects using electroluminescence images[J]. Energy, 267, 126605(2023).

    [79] MELLIT A. An embedded solution for fault detection and diagnosis of photovoltaic modules using thermographic images and deep convolutional neural networks[J]. Engineering Applications of Artificial Intelligence, 116, 105459(2022).

    [80] HUANG C, ZHANG Z Y, WANG L. PSOPruner: PSO-based deep convolutional neural network pruning method for PV module defects classification[J]. IEEE Journal of Photovoltaics, 12, 1550-1558(2022).

    [81] FAN T, SUN T, XIE X Y et al. Automatic micro-crack detection of polycrystalline solar cells in industrial scene[J]. IEEE Access, 10, 16269-16282(2022).

    [82] ET-TALEBY A, CHAIBI Y, ALLOUHI A et al. A combined convolutional neural network model and support vector machine technique for fault detection and classification based on electroluminescence images of photovoltaic modules[J]. Sustainable Energy, Grids and Networks, 32, 100946(2022).

    [83] LI X, YANG Q, WANG J et al. Intelligent fault pattern recognition of aerial photovoltaic module images based on deep learning technique[J]. Journal of Systemics, Cybernetics and Informatics, 16, 67-71(2018).

    [84] ZHANG X, HOU T, HAO Y W et al. Surface defect detection of solar cells based on multiscale region proposal fusion network[J]. IEEE Access, 9, 62093-62101(2093).

    [85] LIU L X, ZHU Y F, RAHMAN M RUR et al. Surface defect detection of solar cells based on feature pyramid network and GA-Faster-RCNN[C], 292-297(2019).

    [86] ZHANG X, HAO Y W, SHANGGUAN H et al. Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks[J]. Infrared Physics and Technology, 108, 103334(2020).

    [87] SU B Y, CHEN H Y, CHEN P et al. Deep learning-based solar-cell manufacturing defect detection with complementary attention network[J]. IEEE Transactions on Industrial Informatics, 17, 4084-4095(2021).

    [88] SU B Y, CHEN H Y, ZHOU Z. BAF-detector: an efficient CNN-based detector for photovoltaic cell defect detection[J]. IEEE Transactions on Industrial Electronics, 69, 3161-3171(2022).

    [89] WANG J Y, ZHAO B, YAO X S. PV abnormal shading detection based on convolutional neural network[C], 1580-1583(2020).

    [90] [90] 唐政,张会林,马立新. 密集主干网络算法对太阳能电池的缺陷检测[J]. 激光与光电子学进展, 2023,60(14):246-252.TANGZ, ZHANGH L, MAL X. Defect detection for solar cells using dense backbone network algorithm[J]. Laser & Optoelectronics Progress, 2023,60(14):246-252.(in Chinese)

    [91] CHEN H Y, SONG M Y, ZHANG Z Z et al. Detection of surface defects in solar cells by bidirectional-path feature pyramid group-wise attention detector[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5025609(2022).

    [92] ZHANG M, YIN L J. Solar cell surface defect detection based on improved YOLO v5[J]. IEEE Access, 10, 80804-80815(2022).

    [93] [93] 陈亚芳, 廖飞, 黄新宇, 等. 多尺度YOLOv5的太阳能电池缺陷检测[J]. 光学 精密工程, 2023, 31(12): 1804-1815. doi: 10.37188/OPE.20233112.1804CHENY F, LIAOF, HUANGX Y, et al. Multi-scale YOLOv5 for solar cell defect detection[J]. Opt. Precision Eng., 2023, 31(12): 1804-1815.(in Chinese). doi: 10.37188/OPE.20233112.1804

    [94] BALC? O? LU Y S, SEZEN B, CUBUKCU CERASI C. Solar cell busbars surface defect detection based on deep convolutional neural network[J]. IEEE Latin America Transactions, 21, 242-250(2023).

    [95] XIE X Y, LIU H, NA Z X et al. DPiT: detecting defects of photovoltaic solar cells with image transformers[J]. IEEE Access, 9, 154292-154303(2021).

    [96] JUMABOEV S, JURAKUZIEV D, LEE M. Photovoltaics plant fault detection using deep learning techniques[J]. Remote Sensing, 14, 3728(2022).

    [97] WANG C H, CHEN H Y, ZHAO S S et al. Efficient and refined deep convolutional features network for the crack segmentation of solar cell electroluminescence images[J]. IEEE Transactions on Semiconductor Manufacturing, 35, 610-619(2022).

    [98] [98] 王延年, 刘宏涛, 刘航宇, 等. 基于改进U-Net的太阳能电池图像缺陷检测方法[J]. 电子测量技术, 2021, 44(14): 117-121.WANGY N, LIUH T, LIUH Y, et al. Solar cell image defect detection method based on improved U-Net[J]. Electronic Measurement Technology, 2021, 44(14): 117-121.(in Chinese)

    [99] RAHMAN M R U, CHEN H Y. Defects inspection in polycrystalline solar cells electroluminescence images using deep learning[J]. IEEE Access, 8, 40547-40558(2020).

    [100] [100] 王盛, 吴浩, 彭宁, 等. 改进U2-Net的太阳能电池片缺陷分割方法[J]. 国外电子测量技术, 2023, 42(2): 177-184.WANGS, WUH, PENGN, et al. Improved U2-Net defect segmentation method for solar cells[J]. Foreign Electronic Measurement Technology, 2023, 42(2): 177-184.(in Chinese)

    [101] BALZATEGUI J, ECIOLAZA L, ARANA-AREXOLALEIBA N. Defect detection on polycrystalline solar cells using electroluminescence and fully convolutional neural networks[C], 949-953(2020).

    [102] SOHAIL A, ISLAM NUL, UL HAQ A et al. Fault detection and computation of power in PV cells under faulty conditions using deep-learning[J]. Energy Reports, 9, 4325-4336(2023).

    [103] TIAN S S, LI W J, LI S et al. Image defect detection and segmentation algorithm of solar cell based on convolutional neural network[C], 154-157(2021).

    [104] OTAMENDI U, MARTINEZ I, QUARTULLI M et al. Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules[J]. Solar Energy, 220, 914-926(2021).

    [105] CHEN H, ZHANG A, GONG C Y et al. Fault diagnosis method for photovoltaic panels based on improved ShuffleNet V2 and infrared images[C], 447-451(2022).

    [106] [106] 史册, 南新元. 改进InceptionV3与迁移学习的太阳能电池板缺陷识别[J]. 计算机工程与科学, 2023, 45(4): 646-653. doi: 10.3969/j.issn.1007-130X.2023.04.011SHIC, NANX Y. Improved InceptionV3 and transfer learning for solar panel defect recognition[J]. Computer Engineering & Science, 2023, 45(4): 646-653.(in Chinese). doi: 10.3969/j.issn.1007-130X.2023.04.011

    [107] [107] 刘怀广, 丁晚成, 黄千稳. 基于轻量化卷积神经网络的光伏电池片缺陷检测方法研究[J]. 应用光学, 2022, 43(1): 87-94. doi: 10.5768/jao202243.0103003LIUH G, DINGW C, HUANGQ W. Defects detection method of photovoltaic cells based on lightweight convolutional neural network[J]. Journal of Applied Optics, 2022, 43(1): 87-94.(in Chinese). doi: 10.5768/jao202243.0103003

    [109] [109] 王云艳, 周志刚, 罗帅. 基于数据增强的太阳能电池片缺陷检测[J]. 电子测量与仪器学报, 2021, 35(1): 26-32.WANGY Y, ZHOUZ G, LUOS. Defect detection of solar cell based on data augmentation[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(1): 26-32.(in Chinese)

    [110] [110] 文熙. 基于生成对抗网络的太阳能电池缺陷数据增强与缺陷检测[D]. 天津: 河北工业大学, 2020.WENX. Data Augmentation and Defect Detection of Solar Cells Based on Generative Adversarial Network[D]. Tianjin: Hebei University of Technology, 2020. (in Chinese)

    [111] BALZATEGUI J, ECIOLAZA L, MAESTRO-WATSON D. Anomaly detection and automatic labeling for solar cell quality inspection based on generative adversarial network[J]. Sensors, 21, 4361(2021).

    [112] [112] 李梦园. 深度学习算法在表面缺陷识别中的应用研究[D]. 杭州: 浙江工业大学, 2015.LIM Y. Research and Application of Deep Learning Algorithm in Surface Defect Recognition[D]. Hangzhou: Zhejiang University of Technology, 2015. (in Chinese)

    [113] NI B B, ZOU P G, LI Q et al. Intelligent defect detection method of photovoltaic modules based on deep learning[C]. Smart City (TLICSC 2018, 30, 167-173(2018).

    [114] QIAN X L, LI J, ZHANG J W et al. Micro-crack detection of solar cell based on adaptive deep features and visual saliency[J]. Sensor Review, 40, 385-396(2020).

    [115] QIAN X L, LI J, CAO J D et al. Micro-cracks detection of solar cells surface via combining short-term and long-term deep features[J]. Neural Networks, 127, 132-140(2020).

    [116] MAYR M, HOFFMANN M, MAIER A et al. Weakly supervised segmentation of cracks on solar cells using normalized Lp norm[C], 1885-1889(2019).

    [117] CHEN H Y, HU Q D, ZHAI B S et al. A robust weakly supervised learning of deep Conv-Nets for surface defect inspection[J]. Neural Computing and Applications, 32, 11229-11244(2020).

    [118] LU F F, NIU R, ZHANG Z H et al. A generative adversarial network-based fault detection approach for photovoltaic panel[J]. Applied Sciences, 12, 1789(2022).

    [119] PIERDICCA R, PAOLANTI M, FELICETTI A et al. Automatic faults detection of photovoltaic farms: solAIr, a deep learning-based system for thermal images[J]. Energies, 13, 6496(2020).

    [120] BUERHOP-LUTZ C, DEITSCH S, MAIER A et al. A benchmark for visual identification of defective solar cells in electroluminescence imagery[C], 1287-1289(2018).

    [121] DEITSCH S, BUERHOP-LUTZ C, SOVETKIN E et al. Segmentation of photovoltaic module cells in uncalibrated electroluminescence images[J]. Machine Vision and Applications, 32, 84(2021).

    [122] RODRIGUEZ A R, HOLICZA B, NAGY A M et al. Segmentation and error detection of PV modules[C], 1-4(2022).

    [123] PRATT L, MATTHEUS J, KLEIN R. A benchmark dataset for defect detection and classification in electroluminescence images of PV modules using semantic segmentation[J]. Systems and Soft Computing, 5, 200048(2023).

    [125] SU B Y, ZHOU Z, CHEN H Y. PVEL-AD: a large-scale open-world dataset for photovoltaic cell anomaly detection[J]. IEEE Transactions on Industrial Informatics, 19, 404-413(2023).

    [126] MILLENDORF M, OBROPTA E, VADHAVKAR N. Infrared solar module dataset for anomaly detection[C], 1-9(2015).

    [127] FONSECA ALVES R H, DE DEUS G A, MARRA E G et al. Automatic fault classification in photovoltaic modules using Convolutional Neural Networks[J]. Renewable Energy, 179, 502-516(2021).

    [128] CHEN X, KARIN T, LIBBY C et al. Automatic crack segmentation and feature extraction in electroluminescence images of solar modules[J]. IEEE Journal of Photovoltaics, 13, 334-342(2023).

    [129] CHEN X, KARIN T, JAIN A. Automated defect identification in electroluminescence images of solar modules[J]. Solar Energy, 242, 20-29(2022).

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    Yuqi LIU, Yiquan WU. Review of defect detection algorithms for solar cells based on machine vision[J]. Optics and Precision Engineering, 2024, 32(6): 868

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    Paper Information

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    Received: Sep. 25, 2023

    Accepted: --

    Published Online: Apr. 19, 2024

    The Author Email: Yiquan WU (nuaaimage@163. com)

    DOI:10.37188/OPE.20243206.0868

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