Article
Solar Cell Surface Defect Detection Based on Optimized YOLOv5
The detection of surface defects in solar cells is crucial for ensuring the efficiency and reliability of photovoltaic systems. This paper proposes an automated solar cell surface defect detection system based on an optimized YOLOv5 deep learning model. The system detects and localizes multiple defect types including micro-cracks, scratches, contamination, broken grids, and discoloration in real time. Optimization strategies including custom anchor tuning, mosaic augmentation, and transfer learning maximize detection performance. Experimental results demonstrate mAP@0.5 of 94.3%, precision of 93.1%, recall of 91.8%, with real-time inference at 48 FPS.
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