吉林大学学报（理学版）2026，Vol.64Issue(3)：603-616,14.DOI:10.13413/j.cnki.jdxblxb.2025002
基于YOLOX改进模型的金属表面缺陷检测
Metal Surface Defect Detection Based on Improved YOLOX Model
摘要
Abstract
Aiming at the challenge of balancing model accuracy and inference speed in metal surface defect detection,we proposed an improved SWE-YOLOX detection algorithm based on the YOLOX model.Firstly,in order to solve the problem of large interference of complex backgrounds and significant variation in defect scales,we introduced a channel shuffle attention module to enhance feature expression ability and suppress irrelevant information.Secondly,aiming at the problem of unclear defect edges and weak texture features,we incorporated wavelet convolution to improve the extraction ability of frequency-domain features,thereby enhancing the expression of detailed information.Finally,the original intersection over union(IoU)loss function was replaced with an enhanced intersection over union(EIoU)loss function to optimize the regression accuracy between predicted boxes and ground truth boxes.Experimental results show that the proposed method achieves a mean average precision(mAP)of 76.3％on the NEU-DET dataset,which is 3.86 percentage point higher than that of YOLOX model.It also maintains a fast inference speed without increasing the number of parameters and computational complexity.关键词
YOLOX模型/注意力模块/小波卷积/损失函数/金属表面缺陷Key words
YOLOX model/attention module/wavelet convolution/loss function/metal surface defect分类
信息技术与安全科学引用本文复制引用
车国霖,傅家辉..基于YOLOX改进模型的金属表面缺陷检测[J].吉林大学学报（理学版）,2026,64(3):603-616,14.基金项目
国家重点研发计划子课题项目(批准号:2017YFB0306405). （批准号:2017YFB0306405）
