广西师范大学学报（自然科学版）2026，Vol.44Issue(1)：56-67,12.DOI:10.16088/j.issn.1001-6600.2025022502
基于YOLO11的轻量化PCB缺陷检测算法研究
Research on Lightweight PCB Defect Detection Algorithm Based on YOLO11
摘要
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.关键词
YOLO11/PCB缺陷/轻量化/BiFPN/目标检测Key words
YOLO11/PCB defect/lightweight/BiFPN/object detection分类
信息技术与安全科学引用本文复制引用
黄文杰,罗维平,陈镇南,彭志祥,丁梓豪..基于YOLO11的轻量化PCB缺陷检测算法研究[J].广西师范大学学报（自然科学版）,2026,44(1):56-67,12.基金项目
国家自然科学基金(62103309) （62103309）
湖北省数字化纺织装备重点实验室公开项目(DTL2022007) （DTL2022007）
