Enhanced YOLOv5 Algorithm for Defect Detection in Solar Cells

Photovoltaic cells play a critical role in solar power generation, with defects in these cells significantly impacting energy conversion efficiency. To address challenges in detecting defects of varying scales in solar cells, an enhanced YOLOv5 algorithm is proposed. This algorithm integrates the Convolutional Block Attention Module (CBAM) to improve feature extraction, incorporates the Bi-directional Feature Pyramid Network (BiFPN) for refined feature fusion, and introduces the FasterNet architecture to enhance detection speed without sacrificing performance. Experimental results show a 1.6% increase in mean Average Precision (mAP) compared to the original network, and the effectiveness of the proposed method is further validated against mainstream models.