现代信息科技2025，Vol.9Issue(3)：44-49,6.DOI:10.19850/j.cnki.2096-4706.2025.03.009
基于改进YOLOv8的轻量化焊缝表面缺陷检测方法
Lightweight Weld Surface Defect Detection Method Based on Improved YOLOv8
陈新 1徐辉 1张孜勉 1熊铁军 1陈曾雄1
作者信息
- 1. 中国联合网络通信有限公司湖南省分公司,湖南 长沙 410014
- 折叠
摘要
Abstract
Aiming at the problems of high false detection rate and missed detection rate in steel weld surface defect detection due to factors such as small defect scale,variable morphology and low contrast between defects and background,a lightweight weld surface defect detection method based on improved YOLOv8 model is proposed.Firstly,the Spatial Pyramid Decomposition(SPD)module is introduced into the model backbone network to enhance the model's ability to detect small-scale defects.Secondly,the SimAM is embedded in the feature fusion network to enhance the feature representation ability of the model for low contrast defects.Thirdly,Wise-IoU is used to replace the traditional bounding box regression loss function to optimize the localization accuracy of the model.Finally,the down-sampling method is improved by the ADown module to effectively retain the detailed features of the weld defects.The experimental results show that the detection accuracy,recall rate and mean Average Precision(mAP)of the improved model are increased by 3.7%,1.6%and 3.6%,respectively.Its comprehensive performance is better than the original model and other mainstream object detection models,which provides an effective solution for the deployment of weld defect detection systems in industrial scenarios.关键词
焊缝缺陷检测/YOLOv8/SPD模块/SimAM注意力Key words
weld defect detection/YOLOv8/SPD module/SimAM分类
信息技术与安全科学引用本文复制引用
陈新,徐辉,张孜勉,熊铁军,陈曾雄..基于改进YOLOv8的轻量化焊缝表面缺陷检测方法[J].现代信息科技,2025,9(3):44-49,6.
