Ceramic Surface Defect Detection Algorithm Based on Improved YOLOv5
It propose a ceramic surface defect detection algorithm YOLOv5-G.This algorithm introduces the Global Attention Mechanism(GAM)into the backbone and neck networks based on the YOLOv5 framework.This mechanism can amplify the global interaction features while reducing the information dispersion,and enhances the feature expression capability of the network.Taking α-CIoU as the bounding box regression loss function of the improved algorithm,the loss and gradient of high IoU objects are adaptively weighted upward,so that the model can pay more attention to the targets with high IoU,thus helping to improve the positioning accuracy.The test is carried out on the ceramic surface defect data set of the industrial camera imaging,and the results show that compared with the YOLOv5 model,the Mean Average Precision(mAP50)and Recall rate of the YOLOv5-G model based on α-CIoU are increased by 2.3%and 4.6%respectively.The Mean Average Precision(mAP50),Precision and Recall of the improved algorithm are all significantly improved by 3.9%,1.7%and 5.1%respectively.
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