计算机工程与应用2026，Vol.62Issue(7)：85-95,11.DOI:10.3778/j.issn.1002-8331.2505-0316
工业场景下的钢材表面缺陷实时检测网络
Real-Time Detection Network for Steel Surface Defects in Industrial Scenarios
摘要
Abstract
For defect detection on metal surfaces in industrial production,facing challenges including large variations in defect scales,feature extraction difficulties,and poor real-time inference,this paper proposes an efficient model named MBAC-YOLO(YOLO with multi-head convolution,bio-inspired hybrid attention module and contextual enhancement).It integrates three innovative modules:the multi-head convolution module(MCM),enhancing the model's receptive field and backbone network's local feature extraction;the bio-inspired hybrid attention module(BHAM),strengthening under-standing of spatial/channel edge information and boosting feature representation in the neck network;and the global con-text enhancement module(GCEM),generating adaptive weights and constructing global contextual interaction.Perfor-mance is validated using NEU-DET and GC10-DET datasets.Results demonstrate MBAC-YOLO achieves mAP50 imp-rovements of 5.8 percentage points and 5.3 percentage points,with FPS reaching 185.19 and 182.0,indicating significant advantages in both accuracy and real-time performance.关键词
缺陷检测/特征提取/注意力机制/上下文信息Key words
defect detection/feature extraction/attention mechanism/context information分类
信息技术与安全科学引用本文复制引用
仵大奎,葛承昆,周文举,高艺友..工业场景下的钢材表面缺陷实时检测网络[J].计算机工程与应用,2026,62(7):85-95,11.基金项目
国家自然科学基金(U24A20259). （U24A20259）
