Abstract: 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.

Key words: YOLO11, PCB defect, lightweight, BiFPN, object detection