工业场景下的钢材表面缺陷实时检测网络OA

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.