A New Dual-Branch Network With Global Information for the Surface Defect Detection on Solar PV Wafer

Photovoltaic (PV) wafers are a basic material for solar cells, and surface defect detection for solar PV wafers is necessary to improve the quality of solar PV. However, the complexity of the wafer manufacturing environment causes the surface defects of PV wafers to appear diverse, and it is still challenging in practical applications. In this research, a deep-learning (DL) network based on global information branches and dual-branch feature fusion (DBFF) is proposed for surface defect detection (SDD), named GFYolov7. First, for the issue of the low contrast on the wafer surface samples, the global information branching (GIB) is generated using the multiscale features of the backbone, which improves the information utilization of the backbone. Second, a DBFF module is designed for the GIB module, and a lightweight attention mechanism is used for the fused multiscale features, which serve to highlight defective regions and suppress irrelevant regions. The experimental results of GFYolov7 on NEU-DET and the GC10-DET reach 79.5 and 72.1 mAP and that on the real-world PV wafer dataset is 72.7 mAP. By comparing with other DL methods, GFYolov7 has been demonstrated that it has good detection ability and its detection speed can reach 61 FPS.