液晶与显示2025，Vol.40Issue(10)：1545-1556,12.DOI:10.37188/CJLCD.2025-0137
基于改进YOLO11的印刷电路板表面缺陷检测算法
Surface defect detection on printed circuit boards based on improved YOLO11
摘要
Abstract
To address the challenges of low accuracy,false detections,and missed detections in PCB defect inspection under scenarios with small defect sizes,complex circuit backgrounds,and irregular defect shapes,this paper proposes a surface defect detection method based on an improved YOLOv11 architecture.First,a multi-scale convolutional attention(MSCA)mechanism is integrated into the backbone to enhance the model's sensitivity to subtle defects.Second,the neck structure is replaced with the high-level screening feature pyramid network(HS-FPN),which improves defect detection across sizes by using channel attention(CA)and selective feature fusion(SFF).Third,the original C3K2 module is enhanced by combining ConvFormer with a convolutional gated linear unit(CGLU),which improves feature representation while reducing computational cost.Furthermore,the unified-IoU(UIoU)loss function is applied to dynamically adjust the weighting of candidate boxes,enhancing both localization accuracy and convergence speed.Extensive experiments are conducted on a self-constructed PCB dataset with six defect categories.The proposed model achieves a mean average precision(mAP)of 89.1%,surpassing the baseline YOLOv11 by 5.3%in mAP.In addition,the precision and recall are improved by 2.3%and 6.1%,respectively,while the model's parameter count is reduced by 28.3%.The results demonstrate the effectiveness and practical potential of the proposed approach for real-world PCB defect detection tasks.关键词
图像处理/PCB缺陷检测/YOLO11/多尺度卷积注意力机制/高效特征金字塔网络Key words
image processing/PCB defect detection/YOLO11/multi-scale convolutional attention/HS-FPN分类
信息技术与安全科学引用本文复制引用
杨彦萍,高军伟,刘兆龙,邢荣鑫..基于改进YOLO11的印刷电路板表面缺陷检测算法[J].液晶与显示,2025,40(10):1545-1556,12.基金项目
山东省自然科学基金(No.ZR2019MF063)Supported by Natural Science Foundation of Shandong Province(No.ZR2019MF063) （No.ZR2019MF063）
