Surface Defect Detection Method of Hot Rolled Steel Based on Improved Lightweight SE-Yolov4 Algorithm
Aiming at the problems of low detection accuracy and slow detection speed of traditional hot rolled steel surface defect detection,and the problems of slow detection speed and poor robustness of traditional machine learning detection,a surface defect detection method based on improved lightweight SE-Yolov4 hot rolled steel is proposed.The SE-Yolov4 network is composed of SE-Yolov4 network,which is embedded in the residual network of each layer of the Yolov4 backbone feature extraction network CSPDarknet53 to selectively gather effective information.At the same time,after the backbone feature network outputs different feature information before and after the Spatial Pooling Pyramid,the number of convolution layers is increased,and the network structure is complex;SE-Yolov4 algorithm embeds lightweight mobileNet v3 structure to reduce the amount of model parameters and improve the detection speed.The experimental results show that the mAP value of the improved algorithm in the testset reaches 93.02%,which improves the detection accuracy by 7.2%and the detection speed by nearly three times compared with Yolov4 algorithm.
hot rolled steelYolov4SENetsurface defect detectionconvolutional neural networkMobileNet v3
NETL
NSTL
万方数据
维普
