Article(id=1309475138521886852, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475137360060880, articleNumber=null, orderNo=null, doi=10.19710/J.cnki.1003-8817.20250288, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790132801894, onlineDateStr=2026-09-23, pubDate=1781884800000, pubDateStr=2026-06-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790132801894, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790132801894, creator=13701087609, updateTime=1790132801894, updator=13701087609, issue=Issue{id=1309475137360060880, tenantId=1146029695717560320, journalId=1189873562199433220, year='2026', volume='', issue='6', pageStart='1', pageEnd='72', issueExtLink='null', onlineDate='null', pubDate='1781884800000', pubDateStr='2026-06-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1790132801618, creator='13701087609', updateTime=1790132983936, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309475902124286252, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475137360060880, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309475902124286253, tenantId=1146029695717560320, journalId=1189873562199433220, issueId=1309475137360060880, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=23, endPage=30, ext={EN=ArticleExt(id=1309475138744184965, articleId=1309475138521886852, tenantId=1146029695717560320, journalId=1189873562199433220, language=EN, title=Intelligent Detection System for Automotive White Body Process Based on Computer Vision and Deep Learning Technologies, columnId=null, journalTitle=Automobile Technology & Material, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the challenges of low efficiency, high miss rate, and insufficient imaging stability associated with manual visual inspection in traditional automotive Body-In-White (BIW) joining processes, an intelligent inspection system based on computer vision and deep learning technologies is developed. An industrial camera imaging system is constructed with optimized optical parameters, and image preprocessing is realized using automatic masking techniques based on interactive image segmentation. Object detection is performed by incorporating an end-to-end object detection model (DETR). A full-stack data management architecture encompassing data acquisition, processing, storage, and visualization is established. Experimental results demonstrate that the system achieves a detection accuracy of over 99%, reduces task completion time by 80%, and lowers the miss rate by 67%. Both inspection efficiency and result consistency are significantly improved, satisfying the requirements for batch inspection of BIWs.

, authors=Jiaqi Wang, Yang Shi, Jin An, Jiaxin Li, Sicheng Wang, authorsList=Jiaqi Wang, Yang Shi, Jin An, Jiaxin Li, Sicheng Wang, authorCompany=null, correspAuthors=Sicheng Wang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309475139000037510, articleId=1309475138521886852, tenantId=1146029695717560320, journalId=1189873562199433220, language=CN, title=基于计算机视觉与深度学习技术的汽车白车身工艺智能检测系统, columnId=1309475138538668644, journalTitle=汽车工艺与材料, columnName=整车先进检测技术专题, runingTitle=null, highlight=null, articleAbstract=

为解决传统汽车白车身连接工艺质量控制中人工目检效率低、漏检率高的问题，构建基于计算机视觉与深度学习技术的智能检测系统。通过搭建工业相机成像系统并优化光学参数，采用交互式图像分割自动掩码技术完成图像预处理，结合端到端目标检测模型（DETR）实现目标检测，搭建涵盖采集、处理、存储与可视化的全栈数据管理架构。试验表明，系统检测准确率稳定在99%以上，任务完成时间缩短80%，漏检率降低67%，检测效率与结果一致性大幅提升，可满足白车身批量检测需求。

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王家琦（1989—），男，工程师，硕士学位，研究方向为人工智能、智能制造，wangjq05@bbac.com.cn。

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王家琦（1989—），男，工程师，硕士学位，研究方向为人工智能、智能制造，wangjq05@bbac.com.cn。

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