测试技术学报2025，Vol.39Issue(4)：430-438,9.DOI:10.62756/csjs.1671-7449.2025054
基于改进YOLOv8的钢材质表面缺陷检测
Surface Defect Detection of Steel Materials Based on Improved YOLOv8
张惠 1韩跃平 1李瑞红2
作者信息
- 1. 中北大学 信息与通信工程学院,山西 太原 030051
- 2. 中北大学 软件学院,山西 太原 030051
- 折叠
摘要
Abstract
With the deep integration of new generation information technology and the manufacturing industry,it has triggered tremendous changes in the manufacturing industry,and the detection of surface defects in industrial production has become increasingly important.A YOLOv8-based improved model algorithm is proposed to address the issues of missed and false detections,as well as inaccurate detection accuracy in steel surface defect detection.Firstly,in the backbone network,the original C2f module has been integrated with the spatial and channel reconstruction convolution module to reduce inherent redun-dancy in dense model parameters and further develop lightweight network models.Secondly,the path aggregation network is replaced by the bidirectional feature pyramid network in the Neck stage,which is intended to improve the detection performance of algorithms by using effective feature fusion methods.Finally,CIOU in YOLOv8 is replaced by Wise-IOUv1 to optimize the loss function.Experimental results show that the improved YOLOv8 algorithm has increased mAP by 8.1 percentage points,reduced computational complexity by 9.6 percentage points,and improved detection speed by 8.7 percentage points,compared to the original algorithm.This detection performance is better than the original algo-rithm,which greatly improved the detection performance of steel surface defect detection.关键词
缺陷检测/YOLOv8/空间和通道重建卷积/特征融合/Wise-IOUv1Key words
defect detection/YOLOv8/spatial and channel reconstruction convolution/feature fusion/Wise-IOUv1分类
计算机与自动化引用本文复制引用
张惠,韩跃平,李瑞红..基于改进YOLOv8的钢材质表面缺陷检测[J].测试技术学报,2025,39(4):430-438,9.
