[1] Supriyono, Toto, Ghazali Omar, Noreffendy Tamaldin, Priyono Soetikno, Muhammad Rizki Sumartono, Angesta Romano, and Mohamad Yamin. “Performance comparison of monocrystalline and polycrystalline photovoltaic modules before testing with a cooling system.” Cogent Engineering 11, no. 1, 2024, 2430426.
[2] Kajari-Schršder S, Kunze I, Kšntges M. “Criticality of cracks in PV modules.” Energy Procedia 2012;27:658–63.
[3] S. Hassan and M. Dhimish, “A Survey of CNN-Based Approaches for Crack Detection in Solar PV Modules: Current Trends and Future Directions,” Solar, vol. 3, no. 4, pp. 663-683, 2023.
[4] M. Karimi, J. S. Fada, M. A. Hossain, S. Yang, T. J. Peshek, J. L. Braid, and R. H. French, ‘‘Automated pipeline for photovoltaic module electroluminescence image processing and degradation feature classification,’’ IEEE J. Photovolt., vol. 9, no. 5, pp. 1324–1335, Sep. 2019.
[5] M. W. Akram, G. Li, Y. Jin, X. Chen, C. Zhu, X. Zhao, A. Khaliq, M. Faheem, and A. Ahmad, ‘‘CNN based automatic detection of photovoltaic cell defects in electroluminescence images,’’ Energy, vol. 189, Dec. 2019, Art. no. 116319.
[6] S. Deitsch, V. Christlein, S. Berger, C. Buerhop-Lutz, A. Maier, F. Gallwitz, and C. Riess, ‘‘Automatic classification of defective photovoltaic module cells in electroluminescence images,’’ Sol. Energy, vol. 185, pp. 455–468, Jun. 2019.
[7] Lu, S. D., Wang, M. H., Wei, S. E., Liu, H. D., & Wu, ‘‘Photovoltaic module fault detection based on a convolutional neural network,’’ Processes, vol. 9, no. 9, p. 1635, Sep. 2021.
[8] X. Chen, T. Karin, and A. Jain, ‘‘Automated defect identification in electroluminescence images of solar modules,’’ Sol. Energy, vol. 242, pp. 20–29, Aug. 2022.
[9] Breitenstein, O., Khanna, A., Augarten, Y., Bauer, J., Wagner, J. M., & Iwig, K. “Quantitative evaluation of electroluminescence images of solar cells.” physica status solidi (RRL)–Rapid Research Letters, 4(1‐2), 7-9, 2010.
[10] K. Yildiz, A. Buldu, M. Demetgul, and Z. Yildiz, ‘‘A novel thermal-based fabric defect detection technique,’’ J. Textile Inst., vol. 106, no. 3, pp. 275–283, Mar. 2015
[11] T. Fan, T. Sun, X. Xie, H. Liu and Z. Na, “Automatic Micro-Crack Detection of Polycrystalline Solar Cells in Industrial Scene,” in Proc. IEEE Access, vol. 10, pp. 16269-16282, 2022.
[12] E. mousavian , D. Qashqai and S. B. Shokouhi, “Discriminative Cross-Modal Attention Approach for RGB-D Semantic Segmentation,” Computer and Knowledge Engineering, 2025, 43-52, doi: 10.22067/cke.2025.88682.1117.
[13] D. G. Lowe, ‘‘Distinctive image features from scale-invariant keypoints,’’ Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004, doi: 10.1023/B:VISI.0000029664.99615.94.
[14] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, ‘‘Speeded-up robust features (SURF),’’ Comput. Vis. Image Understand., vol. 110, no. 3, pp. 346–359, Jun. 2008, doi: 10.1016/j.cviu.2007.09.014.
[15] 24B24B24BN. Dalal and B. Triggs, ‘‘Histograms of oriented gradients for human detection,’’ in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), San Diego, CA, USA, Jun. 2005, pp. 886–893, doi: 10.1109/CVPR.2005.177.
[16] T. Ojala, M. Pietikäinen, and T. Mäenpää, ‘‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,’’ in Proc. IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002, doi: 10.1109/TPAMI.2002.1017623.
[17] O. D. Singh, S. Gupta, and S. Dora, “Segmentation technique for the detection of Micro cracks in solar cell using support vector machine,” in Proc. Multimedia Tools and Applications, vol. 82, pp. 32091-32116, 2023.
[18] M. Demant, T. Welschehold, M. Oswald, S. Bartsch, T. Brox, S. Schoenfelder, and S. Rein, ‘‘Microcracks in silicon wafers I: Inline detection and implications of crack morphology on wafer strength,’’ in Proc. IEEE J. Photovolt., vol. 6, no. 1, pp. 126–135, Jan. 2016.
[19] D.P. Winston, M.S. Murugan, R.M. Elavarasan, R. Pugazhendhi, O.J. Singh, P. Murugesan, M. Gurudhachanamoorthy, E. Hossain, “Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM”, in Proc . IEEE Access, 9, 2021.
[20] F. Demir, “Enhancing Defect Classification in Solar Panels With Electroluminescence Imaging and Advanced Machine Learning Strategies,” in IEEE Access, vol. 13, pp. 58481-58495, 2025.
[21] L. Jasińska, M. Zagórski, J. Domaradzki, M. Maziuk and P. Chodasewicz, “Application of Artificial Intelligence for Selected Photovoltaic Faults Detection Based on Electroluminescence Images,” in IEEE Access, vol. 13, pp. 126030-126042, 2025.
[22] Al-Otum, Hazem Munawer. “Classification of anomalies in electroluminescence images of solar PV modules using CNN-based deep learning,” in Solar Energy, 2024, 112803.
[23] Balzategui, Julen, Luka Eciolaza, Nestor Arana-Arexolaleiba, Jon Altube, Jean-Philippe Aguerre, Iñaki Legarda-Ereño, and Aitor Apraiz. “Semi-automatic quality inspection of solar cell based on Convolutional Neural Networks.” In Proc. 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 529-535. IEEE, 2019.
[24] Tella, H., Hussein, A., Rehman, S., Liu, B., Balghonaim, A. and Mohandes, M., “Solar photovoltaic panel cells defects classification using deep learning ensemble methods”. in Case Studies in Thermal Engineering, 2025, 66, p.105749.
[25] F. M. A. Mazen, R. A. A. Seoud and Y. O. Shaker, “Deep Learning for Automatic Defect Detection in PV Modules Using Electroluminescence Images,” in Proc. IEEE Access, vol. 11, pp. 57783-57795, 2023.
[26] Liu, Qing, Min Liu, Chenze Wang, and QM Jonathan Wu., “An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images,” in Solar Energy, 2024, 112245.
[27] Y. Jiang and C. Zhao, “Attention classification-and-segmentation network for micro-crack anomaly detection of photovoltaic module cells,” in Proc. Solar Energy, vol. 238, pp. 291-304, 2022.
[28] O. D. Singh, A. Malik, V. Yadav, and S. Gupta, “Deep Segmenter system for recognition of micro cracks in solar cell,” in Proceedings of Multimedia Tools and Applications, vol. 80, no. 5, pp. 6509-6533, 2021.
[29] H. Gasparyan, S. Agaian and S. Wu, “Efficient Lightweight Networks for Solar Panel Fault Classification Using EL and RGB Imagery,” in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-11, 2025, Art no. 3518311.
[30] He, K., Zhang, X., Ren, S., & Sun, J. “Deep residual learning for image recognition”. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
[31] Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. “Imagenet: A large-scale hierarchical image database.” in Proc. IEEE conf. compu. Vis. pattern recognit., 2009, pp. 248-255.
[32] Sun M, Lv S, Zhao X, Li R, Zhang W, Zhang X. “Defect detection of photovoltaic modules based on convolutional neural network. In: International conference on machine learning and intelligent communications,” in Springer, 2017, p. 122–32.
[33] Bartler A, Mauch L, Yang B, Reuter M, Stoicescu L. “Automated detection of solar cell defects with deep learning.” In Proceedings of 26th European signal processing conference (EUSIPCO), IEEE, 2018, p. 2035–9.
[34] Tang W, Yang Q, Xiong K, Yan W. “Deep learning based automatic defect identification of photovoltaic module using electroluminescence images,” in Proc. Sol. Energy, 2020;201:453–60.
[35] Zhang, J., Chen, X., Wei, H. and Zhang, K., “A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation,” in proc. Applied Energy, 2024, 355, p.122184.
[36] Demirci, Mustafa Yusuf, Nurettin Beşli, and Abdülkadir Gümüşçü. “An improved hybrid solar cell defect detection approach using Generative Adversarial Networks and weighted classification.” Expert Systems with Applications 252, 2024, 124230.
[37] Selvaraju RR, Das A, Vedantam R, Cogswell M, Parikh D, Batra D. “Grad-CAM: Why did you say that? Visual explanations from deep networks via gradient-based localization.”, arXiv preprint, arXiv:1610.02391, 2016.
