2025 • In Fujita, Hamido (Ed.) Advances and Trends in Artificial Intelligence. Theory and Applications - 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025, Proceedings
[en] Automated defect detection in photovoltaic (PV) modules is essential for their maintenance and efficiency, yet challenges such as limited and imbalanced datasets hinder the adoption of high-accuracy systems. This study evaluates six semantic segmentation architectures based on U-Net and SegNet, paired with VGG16, MobileNet, and ResNet50 encoders, and trained on the 29-class dataset of PV module electroluminescence (EL) images. To address dataset imbalance, custom class weights were applied for all the feature and defect classes. VGG16-UNet outperformed other architectures, achieving a mean intersection over union (IoU) of 0.663 for feature classes and 0.326 across defect classes. In particular, it improved the detection of rare defects, such as dead cell, by 0.129 IoU. While previous research focused on a specific subset of classes, this study is the first to provide a comprehensive performance evaluation across all classes. It establishes a baseline for multi-class semantic segmentation in PV defect detection, laying the groundwork for further industrial applications such as in-field defect detection integrated into solar panel cleaning robots. Our implementation is publicly available at https://github.com/sntubix/pv-defect-segmentation, facilitating further research and development.
Disciplines :
Computer science
Author, co-author :
HANIFI, Shiva ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
JAFARNEJAD, Sasan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
Cormier, Mathieu ; SolarCleano, Grass, Luxembourg
FRANK, Raphaël ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
External co-authors :
no
Language :
English
Title :
Multi-class Semantic Segmentation of Photovoltaic Module Defects and Features: Towards Industrial Robotic Applications
Publication date :
12 July 2025
Event name :
38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Event place :
Kitakyushu, Jpn
Event date :
01-07-2025 => 04-07-2025
Audience :
International
Main work title :
Advances and Trends in Artificial Intelligence. Theory and Applications - 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025, Proceedings
Editor :
Fujita, Hamido
Publisher :
Springer Science and Business Media Deutschland GmbH
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017). https://doi.org/10.1109/TPAMI.2016. 2644615
Buerhop-Lutz, C., et al.: A benchmark for visual identification of defective solar cells in electroluminescence imagery. In: 35th European PV Solar Energy Conference and Exhibition, pp. 1287–1289 (2018)
Chadly, A., Moawad, K., Salah, K., Omar, M., Mayyas, A.: State of global solar energy market: overview, China’s role, challenges, and opportunities (2024). https://doi.org/10.1016/j.horiz.2024.100108
Deitsch, S., et al.: Automatic classification of defective photovoltaic module cells in electroluminescence images. Sol. Energy 185, 455–468 (2019). https://doi.org/10.1016/J.SOLENER.2019.02.067
Fioresi, J., et al.: Automated defect detection and localization in photovoltaic cells using semantic segmentation of electroluminescence images. IEEE J. Photovoltaics 12(1), 53–61 (2022). https://doi.org/10.1109/JPHOTOV.2021.3131059
Grisanti, M., Spatafora, M., Ortis, A., Battiato, S.: E-ELPV: extended ELPV dataset for accurate solar cells defect classification. In: Lecture Notes in Networks and Systems 822, 837–848 (2024). https://doi.org/10.1007/978-3-031-47721-8_55
Gupta, D.: Image Segmentation Keras: implementation of SegNet, FCN, UNet, PSPNet and other models in Keras (2023). https://arxiv.org/abs/2307.13215v1
Henrique, R., Alves, F., Antero De Deus Júnior, G., Marra, E.G., Lemos, R.P.: Automatic fault classification in photovoltaic modules using convolutional neural networks (2021). https://doi.org/10.1016/j.renene.2021.07.070
Hijjawi, U., Lakshminarayana, S., Xu, T., Piero, G., Fierro, M., Rahman, M.: A review of automated solar photovoltaic defect detection systems: approaches, challenges, and future orientations. Solar Energy 266, 112186 (2023). https://doi. org/10.1016/j.solener.2023.112186, http://creativecommons.org/licenses/by/4.0/
Hoyer, L., Dai, D., Van Gool, L.: DAFormer: improving network architectures and training strategies for domain-adaptive semantic segmentation (2021). http://arxiv.org/abs/2111.14887
Huang, J., Arriffin, S.A., Chen, Y., Lin, J., Xu, W.: A Novel MoCo-based self-supervised learning framework for solar panel defect detection. IEEE Access., 1– 1 (2025). https://doi.org/10.1109/ACCESS.2025.3529701, https://ieeexplore.ieee. org/document/10840178/
Iqbal, J., Al-Zahrani, A., Alharbi, S.A., Hashmi, A.: Robotics inspired renewable energy developments: prospective opportunities and challenges. IEEE Access 7, 174898–174923 (2019). https://doi.org/10.1109/ACCESS.2019.2957013
Joe, S., et al.: SEiPV-Net: an efficient deep learning framework for autonomous multi-defect segmentation in electroluminescence images of solar photovoltaic modules. Energies 16(23), 7726 (2023). https://doi.org/10.3390/EN16237726
Kar, I., Mukhopadhyay, S., Ralte, Z.: Enhanced solar panel multiclass fine grained classification using attention distilled self-supervised learning and combined loss. In: 10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024, pp. 133–139 (2024). https://doi.org/10.1109/ICACCS60874.2024.10717196
Köntges, M., et al.: Review of failures of photovoltaic modules (2014)
Li, F., et al.: Mask DINO: towards a unified transformer-based framework for object detection and segmentation. Technical report. https://github.com/IDEA-
Mazen, F.M.A., Seoud, R.A.A., Shaker, Y.O.: Deep learning for automatic defect detection in pv modules using electroluminescence images. IEEE Access 11, 57783– 57795 (2023). https://doi.org/10.1109/ACCESS.2023.3284043
Mazen Ali Mazen, F., Shaker, Y.O., Ahmed Abul Seoud, R.: Attention-based Seg-Net: toward refined semantic segmentation of PV modules defects. IEEE Access 12, 100792–100804 (2024). https://doi.org/10.1109/ACCESS.2024.3431098
Maziuk, M., Jasińska, L., Domaradzki, J., Chodasewicz, P.: Imaging methods of detecting defects in photovoltaic solar cells and modules: a survey. Metrol. Measur. Syst. 30(3), 381–401 (2023). https://doi.org/10.24425/mms.2023.146426
Millendorf, M., Obropta, E., Vadhavkar, N.: Infrared solar module dataset for anomaly detection. https://github.com/RaptorMaps/
Paulin, G., Ivasic-Kos, M.: Review and analysis of synthetic dataset generation methods and techniques for application in computer vision. Artif. Intell. Rev. 56(9), 9221–9265 (2023). https://doi.org/10.1007/S10462-022-10358-3/FIGURES/10, https://link.springer.com/article/10.1007/s10462-022-10358-3
Pratt, L.: Solar cell defect detection deep learning models for defect detection in electroluminescence images of solar PV modules. Ph.D. thesis, University of the Witwatersrand (2024). https://wiredspace.wits.ac.za/items/188c94a9-0c1e-45f6-92b0-ca0cf6913e71
Pratt, L., Govender, D., Klein, R.: Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation. Renew. Energy 178, 1211–1222 (2021). https://doi.org/10.1016/j.renene.2021.06. 086
Pratt, L., Mattheus, J., Klein, R.: A benchmark dataset for defect detection and classification in electroluminescence images of PV modules using semantic segmentation. Syst. Soft Comput. 5 (2023). https://doi.org/10.1016/j.sasc.2023.200048
Rahman, M.R.U., Chen, H.: Defects inspection in polycrystalline solar cells electroluminescence images using deep learning. IEEE Access 8, 40547–40558 (2020). https://doi.org/10.1109/ACCESS.2020.2976843
Rico Espinosa, A., Bressan, M., Giraldo, L.F.: Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks. Renew. Energy 162, 249–256 (2020). https://doi.org/10.1016/j.renene.2020.07.154
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedi-cal image segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sagaram, S., Didwania, K., Srivastava, L., Kasliwal, A., Kailas, P., Verma, U.: Solar panel segmentation:self-supervised learning solutions for imperfect datasets (2024). https://doi.org/10.3390/rs14215350, https://arxiv.org/abs/2402.12843v3
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings (2014). https://arxiv.org/abs/1409. 1556v6
Sovetkin, E., Achterberg, E.J., Weber, T., Pieters, B.E.: Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules. IEEE J. Photovoltaics 11(2), 444–452 (2021). https://doi.org/10.1109/JPHOTOV.2020.3041240
Su, B., Zhou, Z., Chen, H.: PVEL-AD: a large-scale open-world dataset for photovoltaic cell anomaly detection. IEEE Trans. Ind. Inf. 19(1), 404–413 (2023). https://doi.org/10.1109/TII.2022.3162846
Tella, H., Mohandes, M., Liu, B., Rehman, S., Al-Shaikhi, A.: Deep learning system for defect classification of solar panel cells. In: 14th IEEE International Conference on Computational Intelligence and Communication Networks (2022). https://doi. org/10.1109/cicn.2022.77