Defect detection method in aluminum material surface based on improved YOLOv4
Aiming at the problem of low accuracy and easily missing defects in aluminum surface defect detection,a defect detection method of improved YOLOv4 is proposed.SE is introduced into CSPResblock giving channels weights,which can increase training effect for important information and improve the ability of feature extraction.Using pooling kernels with different aspect ratios is beneficial to retain more short-side information,so SPP is revised to improve the network's ability to detect large aspect ratio defects.PANet is improved to fuse more input shallow feature information from the three outputs of backbone,increasing the ability in detecting small objects.The experiment result shows that mAP of improved YOLOv4 algorithm achieves 79.27%in aluminum surface defect data set,better than other common object detection algorithms.
object detectionaluminum material surface defectYOLOv4attention mechanismmachine vision
万方数据
维普
