重庆科技大学学报（自然科学版）2026，Vol.28Issue(2)：81-93,13.DOI:10.19406/j.issn.2097-4531.2026.02.009
基于多分支特征聚合和大核感知单元的钢材缺陷检测算法
A Steel Surface Defect Detection Algorithm Based on Multi-Branch Feature Fusion and Large-Kernel Perception Unit
摘要
Abstract
To address the issues of low detection accuracy,missed detections and false detections caused by the di-versity of steel defect scales and the complexity of texture structures,we propose a steel defect detection algorithm based on multi-branch feature aggregation and large kernel perception unit.First,a multi-branch feature aggrega-tion method is proposed,which utilizes a third-order heterogeneous convolution fusion strategy to comprehensively capture the multi-scale feature information of steel defects.Second,to enhance the algorithm's ability to model the structural information of target regions,a large kernel perception unit is designed based on a method combining large convolution kernels with spatial dilated convolution,which can effectively model the context information in steel defect regions,thereby achieving weighted enhancement of the original features.Subsequently,we construct a structure-aware enhancement module that strengthens the algorithm's ability to model multi-scale structural features and context information,thereby improving its perception and focus on key defect regions.Finally,the Inner_SIoU loss function is employed to refine the bounding box regression process,which enhances the algorithm's detection capability for steel defect targets and reduces the miss rate.Experimental results demonstrate that on the augmented NEU-DET dataset,the improved algorithm achieved mean average precision,average precision,and recall rates of 88.7％,87.8％,and 81.2％,respectively,representing improvements of 3.8,4.6,and 2.1 percentage points over the YOLOv11n model.This indicates that the improved algorithm has achieved a significant enhancement in detection accuracy.关键词
钢材缺陷检测/多分支特征聚合/异构卷积融合/大核感知单元/结构感知增强Key words
steel defect detection/multi-branch feature aggregation/heterogeneous convolution fusion/large-ker-nel perception unit/structure-aware enhancement分类
信息技术与安全科学引用本文复制引用
雷亮,周华勇,徐山雯,刘学涵,赵锦,陈毅,李亚轩..基于多分支特征聚合和大核感知单元的钢材缺陷检测算法[J].重庆科技大学学报（自然科学版）,2026,28(2):81-93,13.基金项目
重庆市教委科学技术研究项目"基于YOLO-Pose姿态特征检测融合时序的独居老人跌倒识别算法研究"(KJQN202303305) （KJQN202303305）
重庆市属本科高校与中国科学院所属院所合作项目"工业互联网内生安全关键技术研究与协同创新"(HZ2021015) （HZ2021015）
重庆科技大学研究生创新计划项目"工业场景下的钢材表面缺陷检测方法研究"(YKJCX2520928) （YKJCX2520928）
