ResNet-based image processing approach for precise detection of cracks in photovoltaic panels
Abstract Advancing renewable energy solutions requires efficient and durable solar Photovoltaic (PV) modules. A novel mechanism based on Deep Learning (DL) and Residual Network (ResNet) for accurate cracking detection using Electroluminescence (EL) images of PV panels is proposed in this paper. Diff...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09101-z |
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| author | Montaser Abdelsattar Ahmed AbdelMoety Ahmed Emad-Eldeen |
| author_facet | Montaser Abdelsattar Ahmed AbdelMoety Ahmed Emad-Eldeen |
| author_sort | Montaser Abdelsattar |
| collection | DOAJ |
| description | Abstract Advancing renewable energy solutions requires efficient and durable solar Photovoltaic (PV) modules. A novel mechanism based on Deep Learning (DL) and Residual Network (ResNet) for accurate cracking detection using Electroluminescence (EL) images of PV panels is proposed in this paper. Different kinds of ResNet architectures, where ResNet34, ResNet50, and ResNet152 were tested, came out with an F1-Score of 86.63%, 87.37%, and 88.89%, respectively. Although the accuracy for ResNet152 is slightly higher, ResNet34 was chosen as the best model since it gives us a trade-off between detection performance and computational performance. The main contribution in this research is the design of an efficient crack detection system trained on a large PV power dataset composed of 2000 EL images collected from different polycrystalline and monocrystalline cells. Although the dataset has some imperfections, to guarantee the presence of many cell states in each subset, it was split into training (70%), validating (20%), and testing (10%). This research demonstrates the application of advanced DL frameworks for early defect diagnosis from raw data to enhance PV panel maintenance, thereby bolstering the sustainability of solar systems. This research also has a significant impact on the academic industry, offering practical solutions for the renewable energy sector during periods of sustainable energy instability, particularly when new materials supplement PV panel usage. The technology preserves the efficiency of solar modules and encourages clean energy solutions by accurately identifying PV panel faults. The study lays a foundation for the further development of image-based defect detection methods in PV systems. |
| format | Article |
| id | doaj-art-a7ff5367d7d7400fa470d4440819ea5c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a7ff5367d7d7400fa470d4440819ea5c2025-08-20T03:42:35ZengNature PortfolioScientific Reports2045-23222025-07-0115112210.1038/s41598-025-09101-zResNet-based image processing approach for precise detection of cracks in photovoltaic panelsMontaser Abdelsattar0Ahmed AbdelMoety1Ahmed Emad-Eldeen2Electrical Engineering Department, Faculty of Engineering, South Valley UniversityElectrical Engineering Department, Faculty of Engineering, South Valley UniversityRenewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef UniversityAbstract Advancing renewable energy solutions requires efficient and durable solar Photovoltaic (PV) modules. A novel mechanism based on Deep Learning (DL) and Residual Network (ResNet) for accurate cracking detection using Electroluminescence (EL) images of PV panels is proposed in this paper. Different kinds of ResNet architectures, where ResNet34, ResNet50, and ResNet152 were tested, came out with an F1-Score of 86.63%, 87.37%, and 88.89%, respectively. Although the accuracy for ResNet152 is slightly higher, ResNet34 was chosen as the best model since it gives us a trade-off between detection performance and computational performance. The main contribution in this research is the design of an efficient crack detection system trained on a large PV power dataset composed of 2000 EL images collected from different polycrystalline and monocrystalline cells. Although the dataset has some imperfections, to guarantee the presence of many cell states in each subset, it was split into training (70%), validating (20%), and testing (10%). This research demonstrates the application of advanced DL frameworks for early defect diagnosis from raw data to enhance PV panel maintenance, thereby bolstering the sustainability of solar systems. This research also has a significant impact on the academic industry, offering practical solutions for the renewable energy sector during periods of sustainable energy instability, particularly when new materials supplement PV panel usage. The technology preserves the efficiency of solar modules and encourages clean energy solutions by accurately identifying PV panel faults. The study lays a foundation for the further development of image-based defect detection methods in PV systems.https://doi.org/10.1038/s41598-025-09101-zCrack detectionElectroluminescenceImage processingPhotovoltaics |
| spellingShingle | Montaser Abdelsattar Ahmed AbdelMoety Ahmed Emad-Eldeen ResNet-based image processing approach for precise detection of cracks in photovoltaic panels Scientific Reports Crack detection Electroluminescence Image processing Photovoltaics |
| title | ResNet-based image processing approach for precise detection of cracks in photovoltaic panels |
| title_full | ResNet-based image processing approach for precise detection of cracks in photovoltaic panels |
| title_fullStr | ResNet-based image processing approach for precise detection of cracks in photovoltaic panels |
| title_full_unstemmed | ResNet-based image processing approach for precise detection of cracks in photovoltaic panels |
| title_short | ResNet-based image processing approach for precise detection of cracks in photovoltaic panels |
| title_sort | resnet based image processing approach for precise detection of cracks in photovoltaic panels |
| topic | Crack detection Electroluminescence Image processing Photovoltaics |
| url | https://doi.org/10.1038/s41598-025-09101-z |
| work_keys_str_mv | AT montaserabdelsattar resnetbasedimageprocessingapproachforprecisedetectionofcracksinphotovoltaicpanels AT ahmedabdelmoety resnetbasedimageprocessingapproachforprecisedetectionofcracksinphotovoltaicpanels AT ahmedemadeldeen resnetbasedimageprocessingapproachforprecisedetectionofcracksinphotovoltaicpanels |