光学精密工程2024，Vol.32Issue(10)：1622-1637,16.DOI:10.37188/OPE.20243210.1622
并行特征提取和渐进特征融合的计算机主板装配缺陷检测
Computer motherboard assembly defect detection using parallel feature extraction and progressive feature fusion
摘要
Abstract
In view of the complex distribution of component positions,lack of prominent defect targets,and multi-scale issues in the detection of defects in computer motherboard assembly,this paper proposed an end-to-end defect detection algorithm based on parallel feature extraction and cross-attention progres-sive feature fusion.Firstly,a parallel residual feature extraction network was proposed by combining par-tial convolution and visual Transformer.The low computational complexity of partial convolution was uti-lized to extract local features,while the long-distance modeling ability of visual Transformer was utilized to expand the receptive field of the model and enhance the feature extraction ability of the network.Sec-ondly,the cross-attention mechanism was introduced to progressively fuse multi-scale features,and a multi-scale cross-attention progressive feature fusion network was constructed to enhance the feature fusion ability of the detection model.The experimental results on the public dataset show that the mean average accuracy(mAP)of the algorithm reaches 94.63%,which is 4.62%higher than the baseline model YO-LOv5 and is superior to several other advanced models.The detection speed reaches 25 FPS,achieving a good balance between detection accuracy and speed.It provides a fast and effective method for the automa-tion and intelligence of surface assembly defect detection on computer motherboards in the actual industrial environment.关键词
计算机主板装配缺陷检测/并行特征提取/渐进特征融合/视觉Transformer/部分卷积Key words
detection of defects in computer motherboard assembly/parallel feature extraction/progres-sive feature fusion/visual transformer/partial convolution分类
信息技术与安全科学引用本文复制引用
陈俊英,李朝阳,黄汉涛,董戌泽..并行特征提取和渐进特征融合的计算机主板装配缺陷检测[J].光学精密工程,2024,32(10):1622-1637,16.基金项目
国家自然科学基金项目(No.62103316) （No.62103316）
陕西省自然科学基础研究计划项目(No.2023-JC-YB-562) （No.2023-JC-YB-562）
