现代电子技术2024，Vol.47Issue(9)：131-138,8.DOI:10.16652/j.issn.1004-373x.2024.09.024
基于改进YOLOX的钢材表面缺陷检测研究
Steel surface defect detection algorithm based on improved YOLOX
刘毅 1蒋三新1
作者信息
- 1. 上海电力大学 电子与信息工程学院,上海 201306
- 折叠
摘要
Abstract
In view of the unsatisfied feature extraction capability,inadequate feature fusion,and low accuracy in steel surface defect detection in the current single-stage object detection network YOLOX,a steel surface defect detection algorithm based on improved YOLOX is proposed.An improved SE attention mechanism is introduced into the Backbone,adding a pooling layer branch to fuse the weight and strengthen important feature channels.An ASFF(adaptively spatial feature fusion)module is incorporated in the Neck to fully utilize features of different scales and achieve better feature fusion.On the basis of the characteristics of this dataset,the IOU loss function is replaced by EIOU loss function,so as to eliminate inaccurate model positioning and improve the accuracy of defect detection.Experimental results demonstrate that the improved algorithm has good detection performance,which achieves mAP(mean average precision)of 75.66%on the NEU-DET dataset,increasing 3.74%in comparison with that of the original YOLOX algorithm,and 2.76%over that of the YOLOv6 algorithm.Therefore,the detection accuracy of the proposed algorithm outperforms that of the other mainstream algorithms.关键词
YOLOX/单阶段目标检测网络/SE注意力机制/ASFF模块/表面缺陷检测/EIOU损失函数Key words
YOLOX/single-stage object detection network/SE attention mechanism/ASFF module/surface defect detection/EIOU loss function分类
信息技术与安全科学引用本文复制引用
刘毅,蒋三新..基于改进YOLOX的钢材表面缺陷检测研究[J].现代电子技术,2024,47(9):131-138,8.
