重庆工商大学学报（自然科学版）2026，Vol.43Issue(3)：30-37,8.DOI:10.16055/j.issn.1672-058X.2026.0003.004
基于YOLOv8的钢材表面缺陷检测算法
A Steel Surface Defect Detection Algorithm Based on YOLOv8
摘要
Abstract
Objective To address the problems of insufficient detection accuracy and high model complexity in existing steel surface defect detection algorithms,an improved steel surface defect detection algorithm based on YOLOv8,named YOLOv8-RDP,is proposed.Methods First,the RepNCSPELAN4 module was introduced to replace the C2f module in the YOLOv8n model.The feature extraction and integration capability of the model was optimized through parallel processing of features at different scales and their fusion in the final convolution layer.Second,a deformable attention(DA)mechanism was integrated into the backbone network.This mechanism adaptively adjusted the sampling points of convolutional kernels,strengthening the model's ability to capture features of objects with varying shapes and sizes and thereby improving the efficiency of capturing key information.Finally,to reduce the computational resources required by the model,the detection head of the baseline model was modified using the partial convolution(Pconv)module.By leveraging redundancy in feature maps and dynamically adjusting the active area of convolutional kernels based on data availability,the computational load was reduced.Results Experimental results on the NEU-DET dataset show that YOLOv8-RDP achieved an mAP of 78.8％,representing a 1.8％improvement over the baseline model.The number of parameters was reduced to 1.87 M and GFLOPs dropped to 3.5 G,representing decreases of 37.9％and 57.0％compared with the baseline model,respectively.The improved model significantly reduced the demand for computational resources while maintaining high accuracy.Conclusion The YOLOv8-RDP algorithm demonstrates high detection accuracy and low model complexity in steel surface defect detection,showing strong potential for practical application in this field.关键词
缺陷检测/特征融合/可变形注意力/轻量化检测头Key words
defect detection/feature fusion/deformable attention/lightweight detection head分类
矿业与冶金引用本文复制引用
刘昱,彭龑..基于YOLOv8的钢材表面缺陷检测算法[J].重庆工商大学学报（自然科学版）,2026,43(3):30-37,8.基金项目
四川省科技厅项目资助(2019FYG0377). （2019FYG0377）
