Article(id=1309639625132569312, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250527, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1761667200000, revisedDateStr=2025-10-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172018559, onlineDateStr=2026-09-23, pubDate=1779552000000, pubDateStr=2026-05-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172018559, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172018559, creator=13701087609, updateTime=1790172018559, updator=13701087609, issue=Issue{id=1309639624411152909, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='5', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1779552000000', pubDateStr='2026-05-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172018387, creator='13701087609', updateTime=1790232830701, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894689805922601, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894689805922602, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=6, ext={EN=ArticleExt(id=1309639625350673121, articleId=1309639625132569312, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Pedestrian Detection Based on DETR, columnId=1309639624981578254, journalTitle=Automobile Technology, columnName=Special Topic on Full-Stack Environment Perception Technology for Intelligent Vehicles, runingTitle=null, highlight=null, articleAbstract=

To address the issue of low detection accuracy for small pedestrian targets, a pedestrian detection model tailored for complex scenes is proposed based on the DETR algorithm. In the feature extraction stage, the SENet attention mechanism is introduced to enhance channel discrimination ability. By combining image Relative Position Encoding (iRPE) with absolute position encoding, the model's perception of global context and local details is improved. After training on a self-built dataset, experimental results show that the pedestrian recognition accuracy of the proposed scheme reaches 97.2%, with false detection rate and missed detection rate of only 1.7% and 1.1%, respectively. It performs excellently in handling complex scenes such as multi-scale pedestrians, partial occlusion, and low visibility, and its detection performance is significantly better than traditional methods.

, authors=Ping Zhang, Cuinan Yue, Yibin He, Yanlong Wang, authorsList=Ping Zhang, Cuinan Yue, Yibin He, Yanlong Wang, authorCompany=null, correspAuthors=null, 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=1309639625648468707, articleId=1309639625132569312, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进DETR算法的行人检测研究, columnId=1309639625430364898, journalTitle=汽车技术, columnName=智能汽车全栈环境感知技术专题, runingTitle=null, highlight=null, articleAbstract=

为解决小目标行人检测精度低的问题，基于DETR算法提出面向复杂场景的行人检测模型。在特征提取环节引入SENet注意力机制，强化通道判别能力；结合图像相对位置编码（iRPE）与绝对位置编码，提升模型对全局上下文与局部细节的感知能力。经自建数据集训练，试验结果表明：所提出方案的行人识别准确率达97.2%，误检率和漏检率仅为1.7%和1.1%，在处理多尺度行人、部分遮挡及低可见度等复杂场景时表现优异，检测效果均显著优于传统方法。

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