现代信息科技2026，Vol.10Issue(17)：1-6,12,7.DOI:10.19850/j.cnki.2096-4706.2026.17.001
YOLO-PMAA基于YOLOv11的增强PCB小目标缺陷检测算法
YOLO-PMAA:An Enhancing PCB Small Target Defect Detection Algorithm Based on YOLOv11
摘要
Abstract
With the development of PCBs toward higher density,complexity and miniaturization,higher demands are placed on small target defect detection under complex backgrounds.To improve the model's ability to detect small target defects,this study proposes an enhanced PCB small target defect detection algorithm,YOLO-PMAA,based on YOLOv11.First,a pinwheel convolution(PConv)is introduced into the shallow layers of the backbone network to enlarge the receptive field through asymmetric padding and convolution,ensuring that the features of small targets are transmitted to the deeper layers of the network.Second,a multidimensional cascade hybrid attention module(MDCA)is designed,which focuses on important information across the scale,channel and spatial dimensions,thereby strengthening the feature extraction of thin,long and narrow small targets.Finally,an adaptive shape loss function(ASIoU)is designed to improve the detection performance of the model.The algorithm is validated over multiple rounds on the PKU-Market-PCB dataset.Compared with YOLOv11n,it improves precision by 3.0%,recall by 6.2%,mAP@0.5 by 3.8%and mAP@0.5:0.95 by 3.2%,and reduces the number of parameters by 4.25%and the weight file size by 3.82%,effectively improving model performance while balancing model complexity.关键词
PCB缺陷检测/注意力机制/损失函数/特征融合/小目标检测Key words
PCB defect detection/attention mechanism/loss function/feature fusion/small target detection分类
信息技术与安全科学引用本文复制引用
周杨淼..YOLO-PMAA基于YOLOv11的增强PCB小目标缺陷检测算法[J].现代信息科技,2026,10(17):1-6,12,7.基金项目
湖南省教育厅科学研究项目(25C0994) （25C0994）
