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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.13337 |
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| _version_ | 1866910052513742848 |
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| author | Sanghi, Ojas Jost, Norman Pierce, Benjamin G. Cooper, Emma Deane, Isaiah H. Braid, Jennifer L. |
| author_facet | Sanghi, Ojas Jost, Norman Pierce, Benjamin G. Cooper, Emma Deane, Isaiah H. Braid, Jennifer L. |
| contents | Electroluminescence (EL) imaging is widely used to detect defects in photovoltaic (PV) modules, and machine learning methods have been applied to enable large-scale analysis of EL images. However, existing methods cannot assign multiple labels to the same pixel, limiting their ability to capture overlapping degradation features. We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of EL images. The model outputs independent probability maps for cracks, busbars, dark areas, and non-cell regions, enabling accurate co-classification of interacting features such as cracks crossing busbars. The model achieved an accuracy of 98% and has been shown to generalize to unseen datasets. This framework offers a scalable, extensible tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13337 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells Sanghi, Ojas Jost, Norman Pierce, Benjamin G. Cooper, Emma Deane, Isaiah H. Braid, Jennifer L. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Electroluminescence (EL) imaging is widely used to detect defects in photovoltaic (PV) modules, and machine learning methods have been applied to enable large-scale analysis of EL images. However, existing methods cannot assign multiple labels to the same pixel, limiting their ability to capture overlapping degradation features. We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of EL images. The model outputs independent probability maps for cracks, busbars, dark areas, and non-cell regions, enabling accurate co-classification of interacting features such as cracks crossing busbars. The model achieved an accuracy of 98% and has been shown to generalize to unseen datasets. This framework offers a scalable, extensible tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems. |
| title | MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.13337 |