现代制造工程Issue(4)：91-102,12.DOI:10.16731/j.cnki.1671-3133.2026.04.012
基于DCE-YOLO算法的复杂背景下钢材表面缺陷检测研究
Research on steel surface defect detection in complex backgrounds based on DCE-YOLO algorithm
摘要
Abstract
In the field of surface defect detection of steel materials,due to the variable scale and complex background of surface defects,problems such as missed detections,false detections and poor detection accuracy are often occur during detection.To ad-dress these issues,an innovative steel surface defect detection algorithm,named DCE-YOLO,is proposed.Firstly,a dual-path effi-cient downsampling is introduced,where the detail retention branch and the feature focusing branch work together to enhance the model's ability to determine the target location.Secondly,a multi-scale channel spatial self-attention mechanism is designed to enable the backbone network to extract key information more efficiently.Finally,an edge information enhancement module based on Sobel operator is constructed and embedded into C2f block to improve the ability of the model for capturing edges and textures.Experimental results on the NEU-DET dataset show that compared with the baseline model,the proposed DCE-YOLO model,with 2.91 ×106 parameters,improves mAP@0.5 and mAP@0.5:0.95 by 5.1％and 3.1％respectively.Additionally,the experimental results on the GC10-DET dataset confirm the robustness of this method.关键词
钢材表面缺陷/双路径高效下采样/自注意力机制/边缘信息增强Key words
steel surface defects/dual-path efficient downsampling/self-attention mechanism/edge information enhancement分类
信息技术与安全科学引用本文复制引用
刘宗,胡伟..基于DCE-YOLO算法的复杂背景下钢材表面缺陷检测研究[J].现代制造工程,2026,(4):91-102,12.基金项目
国家自然科学基金项目(U1804147) （U1804147）
