电子科技2026，Vol.39Issue(4)：28-34,7.DOI:10.16180/j.cnki.issn1007-7820.2026.04.004
基于动态蛇形卷积的轻量化PCB缺陷检测算法
Lightweight PCB Defect Detection Algorithm Based on Dynamic Snake Convolution
摘要
Abstract
In view of the problems of insufficient detection accuracy and large number of parameters in the ex-isting PCB(Printed Circuit Board)defect detection algorithms,a high-precision lightweight detection algorithm YO-LO-DLN(YOLO-Dynamic Lightweight Network)is proposed based on YOLOv8(You Only Look Once version 8).Dynamic serpentine convolution is introduced into the backbone network to capture more fine structural features of the PCB surface,and the extracted features are processed by the lightweight detection head LSCDHead(Lightweight Shared Convolutional Detection Head).Features of different scales share the same set of convolution to reduce the number of model parameters and perform group normalization operations.The NWD(Normalized Wasserstein Dis-tance)loss function with low sensitivity to the target scale is utilized to evaluate the bounding box similarity,further improving the detection accuracy of the proposed method.In the experiment,YOLO-DLN increases mAP50(mean Average Precision50)and MAP50:95 by 4.4%and 4.6%respectively while using 88.1%of the parameters of the original model.The experimental results show that YOLO-DLN can maintain a high detection accuracy while signifi-cantly reducing the number of model parameters,and is suitable for PCB defect detection in resource-constrained en-vironments.关键词
深度学习/PCB/缺陷检测/YOLO/组归一化/动态蛇形卷积/轻量化检测头/NWDKey words
deep learning/PCB/defect detection/YOLO/group normal/dynamic snake convolution/lightweight-ing detection head/NWD分类
信息技术与安全科学引用本文复制引用
辛长明,王博..基于动态蛇形卷积的轻量化PCB缺陷检测算法[J].电子科技,2026,39(4):28-34,7.基金项目
国家自然科学基金(61803273) （61803273）
辽宁省教育厅青年项目(JYTQN2023284)National Nature Science Foundation of China(61803273) （JYTQN2023284）
Youth Project of Liaoning Provincial Department of Education(JYTQN2023284) （JYTQN2023284）
