J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (5): 1050-1064.doi: 10.1007/s12204-023-2670-3
• Computing & Computer Technologies • Previous Articles Next Articles
蒋文波,郑杭彬,鲍劲松
Received:
2023-04-11
Accepted:
2023-06-20
Online:
2025-09-26
Published:
2023-11-06
CLC Number:
JIANG Wenbo, ZHENG Hangbin, BAO Jinsong. Novel Multi-Step Deep Learning Approach for Detection of Complex Defects in Solar Cells[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 1050-1064.
[1] ZOU C N, ZHAO Q, ZHANG G S, et al. Energy revolution: From a fossil energy era to a new energy era [J]. Natural Gas Industry B, 2016, 3(1): 1-11. [2] KANNAN N, VAKEESAN D. Solar energy for future world: - A review [J]. Renewable and Sustainable Energy Reviews, 2016, 62: 1092-1105. [3] CHEN H Y, ZHAO H F, HAN D, et al. Accurate and robust crack detection using steerable evidence filtering in electroluminescence images of solar cells [J]. Optics and Lasers in Engineering, 2019, 118: 22-33. [4] 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, 2012, 99: 250-262. [5] 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, 2019, 4(4): 499-508. [6] LIU L X, ZHU Y F, UR RAHMAN M R, et al. Surface defect detection of solar cells based on feature pyramid network and GA-faster-RCNN [C]//2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI). Xi'an: IEEE, 2019: 292-297. [7] BARTLER A, MAUCH L, YANG B, et al. Automated detection of solar cell defects with deep learning [C]//2018 26th European Signal Processing Conference. Rome: IEEE, 2018: 2035-2039. [8] BALZATEGUI J, ECIOLAZA L, MAESTRO-WATSON D. Anomaly detection and automatic labeling for solar cell quality inspection based on generative adversarial network [J]. Sensors, 2021, 21(13): 4361. [9] 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, 2015, 29(3): 419-430. [10] 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, 2019, 68(12): 4675-4688. [11] LUO Q W, SUN Y C, LI P C, et al. Generalized completed local binary patterns for time-efficient steel surface defect classification [J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(3): 667-679. [12] 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 & Technology, 2020, 108: 103334. [13] ZHANG M, YIN L J. Solar cell surface defect detection based on improved YOLO v5 [J]. IEEE Access, 2022, 10: 80804-80815. [14] 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, 2020, 201: 453-460. [15] 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, 2021, 17(6): 4084-4095. [16] KUMAR A. Computer-vision-based fabric defect detection: A survey [J]. IEEE Transactions on Industrial Electronics, 2008, 55(1): 348-363. [17] LUO Q W, FANG X X, LIU L, et al. Automated visual defect detection for flat steel surface: A survey [J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(3): 626-644. [18] ZENG N Y, WU P S, WANG Z D, et al. A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-14. [19] LI X, ZHANG W, DING Q, et al. Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation [J]. Journal of Intelligent Manufacturing, 2020, 31(2): 433-452. [20] JAIN S, SETH G, PARUTHI A, et al. Synthetic data augmentation for surface defect detection and classification using deep learning [J]. Journal of Intelligent Manufacturing, 2022, 33(4): 1007-1020. [21] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks [J]. Communications of the ACM, 2020, 63(11): 139-144. [22] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. [23] KARRAS T, AITTALA M, HELLSTEN J, et al. Training generative adversarial networks with limited data [C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. New York: ACM, 2020: 12104-12114. [24] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]//European conference on computer vision. Cham: Springer, 2018: 3-19. [25] GE Z, LIU S T, WANG F, et al. YOLOX: Exceeding YOLO series in 2021 [DB/OL]. (2021-07-18). https://arxiv.org/abs/2107.08430 [26] LI H L, LI J, WEI H B, et al. Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles [DB/OL]. (2022-06-06). https://arxiv.org/abs/2206.02424 [27] SOMEPALLI G, SINGLA V, GOLDBLUM M, et al. Diffusion art or digital forgery? investigating data replication in diffusion models [C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver:. IEEE, 2023: 6048-6058. |
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