RESEARCH ON SURFACE DEFECT DETECTION OF SOLAR CELL WITH IMPROVED YOLOv5 ALGORITHM
To solve the surface defect problem of solar cells,the deep learning model YOLOv5 is optimized and improved.Firstly,in order to make full use of deep,shallow and original feature information and strengthen feature fusion,a feature pyramid network(ScFPN)with cross-connection structure is designed.Secondly,in order to strengthen the fusion of multiple receptive fields,the SPPFCSPC module is constructed based on SPPF,and different receptive fields are obtained through the maximum pooling layer,which improves the robustness of the algorithm for the defect detection of solar cells of different scales.Finally,ASD-IoU is used as the bounding loss function to improve the speed and precision of the bounding regression.The experimental results show that the improved YOLOv5 model mAP@(0.50-0.95)reaches 83.1%,and the average accuracy is increased by 3.3 percentage points compared with the YOLOv5 model,indicating that this model is more suitable for surface defect detection of solar cells.
deep learningsolar celldefectconvolutional neural networkobject detectionimage processing
NETL
NSTL
万方数据
