基于YOLO11的轻量化PCB缺陷检测算法研究OA

To address the issues of low detection accuracy,high model complexity,and excessive computational costs in small-target defect detection of printed circuit boards(PCBs),which hinder deployment on edge devices,a lightweight algorithm based on YOLO11n was proposed.Firstly,the BiMAFPN(Bi-Directional Multi-Branch Auxiliary Feature Pyramid Network)architecture is employed to reconstruct the network structure.Subsequently,the C3k2_Faster module is implemented to reduce model complexity while maintaining detection accuracy.Finally,the LSCD(Lightweight Shared Convolutional Detection)head is introduced to enhance precision.Experimental results demonstrate that the proposed model achieves 93.0%precision and 82.8%recall,with a compact model size of 3.8 MiB.Enhancements include a 0.6 percentage points increase in precision.The mean average precision(mAP)values reach 89.9%(mAP@0.5)and 47.1%(mAP@0.5:0.95),representing improvements of 1.4 and 0.6 percentage points respectively compared with the baseline YOLO11n model while reducing model size,computational complexity,and parameter count by 30.9%,19.0%and 34.6%respectively.These optimizations enable the improved algorithm to maintain competitive detection performance while achieving significant lightweight characteristics,demonstrating strong potential for practical deployment in edge computing environments.