Research on Steel Surface Defect Detection Based on YOLOv5 and ConvNext
In order to solve the problem of slow speed and low accuracy of surface defect detection of indus-trial steel,a detection method based on improved YOLOv5 network was proposed.The ECANet module is added to the FPN feature pyramid module of YOLOv5 network to improve detection precision;The K-Means algorithm is used to recluster the NEU-DET data set,generate three new sets of prior boxes,and reduce the network loss;Aiming at the small target features of steel defects,ConvNext network is applied to the back-bone network of YOLOv5,and ConvNext network is used to extract the small target defect features and en-hance the model learning ability.The experimental results show that compared with the original YOLOv5 model,the map of the improved YOLOv5 model is increased by 3.84%,and the average detection rate is 36.9 frame/s,which can achieve fast and accurate detection and meet the practical application requirements.
万方数据
维普
