Improved YOLOv7-based photovoltaic panel defect detection

To address the challenges of small defect objects and complex background in photovoltaic panel defect detection, an improved YOLOv7 based photovoltaic panel defect detection is proposed in this paper. Coordinate attention mechanism is incorporated to enhance the model's global perception capabilities. Additionally, C-IoU loss function is adopted to optimize training while ensuring improved training accuracy. Experimental results conducted on public dataset demonstrate that the proposed method outperforms baseline object detection algorithms, achieving a mean Average Precision (mAP) of 93.9%.