Abstract
Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images.
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References
Otamendi, U., Martinez, I., Quartulli, M., Olaizola, I.G., Viles, E., Cambarau, W.: Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules. Sol. Energy 220, 914–926 (2021)
Demirci, M.Y., Beşli, N., Gümüşçü, A.: Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in Electroluminescence images. Expert Syst. Appl. 175, 114810 (2021)
Meribout, M., Tiwari, V.K., Herrera, J.P.P., Baobaid, A.N.M.A.: (2023). Solar panel inspection techniques and prospects. Measurement 209, 112466
Akram, M.W., et al.: CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy 189, 116319 (2019)
Tang, W., Yang, Q., Hu, X., Yan, W.: Convolution neural network based polycrystalline silicon photovoltaic cell linear defect diagnosis using electroluminescence images. Expert Syst. Appl. 202, 117087 (2022)
Et-taleby, A., Chaibi, Y., Allouhi, A., Boussetta, M., Benslimane, M.: A combined convolutional neural network model and support vector machine technique for fault detection and classification based on electroluminescence images of photovoltaic modules. Sustain. Energy Grids Netw. 32, 100946 (2022)
Al-Dulaimi, A.A., Guneser, M.T., Hameed, A.A., Márquez, F.P.G., Fitriyani, N.L., Syafrudin, M.: Performance analysis of classification and detection for PV panel motion blur images based on deblurring and deep learning techniques. Sustainability 15(2), 1150 (2023)
Su, B., Chen, H., Zhou, Z.: BAF-detector: an efficient CNN-based detector for photovoltaic cell defect detection. IEEE Trans. Industr. Electron. 69(3), 3161–3171 (2021)
Su, B., Chen, H., Chen, P., Bian, G., Liu, K., Liu, W.: Deep learning-based solar-cell manufacturing defect detection with complementary attention network. IEEE Trans. Industr. Inf. 17(6), 4084–4095 (2020)
PVEL-AD: A Large-Scale Open-World Dataset for Photovoltaic Cell Anomaly Detection
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Redmon, J., Farhadi, A: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)
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Al-Dulaimi, A.A., Hameed, A.A., Guneser, M.T., Jamil, A. (2024). Photovoltaics Cell Anomaly Detection Using Deep Learning Techniques. In: Ortis, A., Hameed, A.A., Jamil, A. (eds) Advanced Engineering, Technology and Applications. ICAETA 2023. Communications in Computer and Information Science, vol 1983. Springer, Cham. https://doi.org/10.1007/978-3-031-50920-9_13
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DOI: https://doi.org/10.1007/978-3-031-50920-9_13
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