HIGH-FIDELITY SOLAR PANELS DEFECT DIAGNOSIS: A YOLOV9 FRAMEWORK LEVERAGING A LARGE CURATED THERMAL DATASET
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2026
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| _version_ | 1866902223974301696 |
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| author | Zeeshan Haider, Ahmad Taher Azar and Yasmin Adel Hagag |
| author_facet | Zeeshan Haider, Ahmad Taher Azar and Yasmin Adel Hagag |
| contents | <p> </p> <p class="MsoNormal"><strong><em><span>Automated defect detection in photovoltaic (PV) modules is a critical component of renewable energy infrastructure maintenance. However, the efficacy of such systems is frequently limited by the quality of training data rather than architectural constraints. A recurring issue in public repositories is data fragmentation and inconsistent labelling, which impedes the development of generalized models. This study introduces a data-centric methodology focused on the curation and unification of a comprehensive PV thermal dataset from multiple sources. To resolve taxonomic discrepancies such as conflicting definitions of” hotspots” and” cracks”, a binary classification protocol (Defect vs. Background) is adopted. This approach eliminates label noise, establishing a consistent foundation for pre-training. The YOLOv9 architecture and its variants (Gelan-c, Gelan-e, Yolov9-c, and Yolov9-e) are employed to validate the dataset’s integrity. Empirical analysis confirms that this unified strategy effectively captures fundamental defect characteristics, providing a robust baseline for future PV monitoring applications.</span></em></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20067683 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HIGH-FIDELITY SOLAR PANELS DEFECT DIAGNOSIS: A YOLOV9 FRAMEWORK LEVERAGING A LARGE CURATED THERMAL DATASET Zeeshan Haider, Ahmad Taher Azar and Yasmin Adel Hagag Photovoltaic Defects; Defects Detection; Renewable Energy; Thermography; Yolov9; Solar Panel Defects. <p> </p> <p class="MsoNormal"><strong><em><span>Automated defect detection in photovoltaic (PV) modules is a critical component of renewable energy infrastructure maintenance. However, the efficacy of such systems is frequently limited by the quality of training data rather than architectural constraints. A recurring issue in public repositories is data fragmentation and inconsistent labelling, which impedes the development of generalized models. This study introduces a data-centric methodology focused on the curation and unification of a comprehensive PV thermal dataset from multiple sources. To resolve taxonomic discrepancies such as conflicting definitions of” hotspots” and” cracks”, a binary classification protocol (Defect vs. Background) is adopted. This approach eliminates label noise, establishing a consistent foundation for pre-training. The YOLOv9 architecture and its variants (Gelan-c, Gelan-e, Yolov9-c, and Yolov9-e) are employed to validate the dataset’s integrity. Empirical analysis confirms that this unified strategy effectively captures fundamental defect characteristics, providing a robust baseline for future PV monitoring applications.</span></em></strong></p> |
| title | HIGH-FIDELITY SOLAR PANELS DEFECT DIAGNOSIS: A YOLOV9 FRAMEWORK LEVERAGING A LARGE CURATED THERMAL DATASET |
| topic | Photovoltaic Defects; Defects Detection; Renewable Energy; Thermography; Yolov9; Solar Panel Defects. |
| url | https://doi.org/10.5281/zenodo.20067683 |