Defect detection algorithm of improved YOLOv5s solar cell
Aiming at the problem of low accuracy of the method for solar cell defect detection,a surface defect detection algorithm based on the improved YOLOv5s solar cell is proposed.First,in order to solve the problem of small target defect detection on the cell sheet,the Contextual Transformer Network(CoT)is proposed,which can provide global contextual information for small targets and the model better at predicting small targets.Secondly,by adding CBAM attention to the C3 module in the Head part,the important channels and spatial locations of the input feature maps can be better captured to improve the performance and robustness of the model.Next,the integrity of feature information is ensured by using CARAFE,a lightweight generalized up-sampling operator,to reduce the loss of feature information during up-sampling.Finally,by using WIoU as the bounding box loss function,the accuracy of the regression can be greatly improved and the convergence of model can be achieved quickly.The experimental results show that compared with the original algorithm,the improved YOLOv5s improves the three indicators of Precision,Recall,and mAP@0.5 by 5.5%,4.1%,and 3.3%respectively,and the detection speed reaches 76 FPS,which meets the requirements of solar cell defect detection.
solar cellYOLOv5scontextual transformer networkCARAFEloss function
万方数据
维普
