现代信息科技2026，Vol.10Issue(18)：45-52,8.DOI:10.19850/j.cnki.2096-4706.2026.18.009
DEW-YOLO:PCB板缺陷检测算法
DEW-YOLO:PCB Defect Detection Algorithm
摘要
Abstract
To address the various defects(such as open circuits,short circuits,missing vias,burrs,and copper leakage)that are prone to occur during the production of PCBs with increasingly complex manufacturing processes,as well as the problems of low detection accuracy for small defects,complex models,and high computational cost,this study proposes an improved detection algorithm based on YOLOv11,named DEW-YOLO.The algorithm utilizes multi-scale features to provide richer and more stable representations and employs an EMA attention module to handle complex background interference and small target recognition.Finally,the CIoU loss function is replaced with the WIoU loss function to capture the feature information more fully,thereby effectively enhancing the overall detection performance and improving the detection of low-quality small targets.Testing shows that the DEW-YOLO model achieves an mAP of 93.8％in PCB defect detection,a 4.9％improvement over YOLOv11,achieving the best performance among similar algorithms.关键词
PCB板/YOLOv11/EMA注意力/目标检测Key words
PCB/YOLOv11/EMA attention/target detection分类
信息技术与安全科学引用本文复制引用
徐森,肖冰瑶,赵洋..DEW-YOLO:PCB板缺陷检测算法[J].现代信息科技,2026,10(18):45-52,8.
