Article(id=1319244967340528235, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250758, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1788796800000, revisedDateStr=2026-09-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1792462110600, onlineDateStr=2026-10-20, pubDate=1790179200000, pubDateStr=2026-09-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792462110600, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792462110600, creator=13701087609, updateTime=1792462110600, updator=13701087609, issue=Issue{id=1319244966216446015, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='9', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1790179200000', pubDateStr='2026-09-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792462110332, creator='13701087609', updateTime=1792735854454, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320393132613681559, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320393132613681560, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=28, endPage=36, ext={EN=ArticleExt(id=1319244967546049132, articleId=1319244967340528235, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Object Detection in Foggy Environments Based on Improved RT-DETR, columnId=1319244967055306816, journalTitle=Automobile Technology, columnName=Special Topic on Lateral Stability and Motion Tracking Control Technology for Steer-by-Wire Vehicles, runingTitle=null, highlight=null, articleAbstract=

To address the degradation of object detection caused by reduced contrast and blurred textures in foggy images, this paper proposes Fog-RT-DETR based on the Real-Time Detection Transformer (RT-DETR). The method integrates an Asymptotic Feature Pyramid Network (AFPN) for multi-scale feature fusion, a Variance-Enhanced Convolutional Block Attention Module (VE-CBAM) for local low-texture perception, and a Feature Degradation Enhancement Module (FDEM) for spatially weighted feature enhancement. Experiments on a foggy-weather dataset constructed from DAWN and ACDC show that Fog-RT-DETR achieves 81.45% mAP@0.5 and 58.78% mAP@0.5∶0.95, improving over the baseline by 2.12 and 3.89 percentage points, respectively. Recall increases by 0.49 percentage points. Ablation experiments show that combining all three modules yields the best detection results in the reported experiments. Channel compression experiments further demonstrate a trade-off between detection accuracy and computational cost.

, authors=Zhaojiawen Wang, Rui Fu, Sirou Qu, Qinyu Sun, authorsList=Zhaojiawen Wang, Rui Fu, Sirou Qu, Qinyu Sun, authorCompany=null, correspAuthors=Rui Fu, 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=1319244969659978349, articleId=1319244967340528235, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进RT-DETR的雾天环境目标检测, columnId=1319244967223078978, journalTitle=汽车技术, columnName=线控转向车辆横向稳定性与运动跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

针对雾天图像对比度下降和目标纹理模糊导致的检测性能退化问题，以实时检测Transformer（RT-DETR）为基准，提出雾天目标检测算法Fog-RT-DETR。该算法引入渐进特征金字塔网络（AFPN）优化多尺度特征融合，利用方差增强卷积块注意力机制（VE-CBAM）感知局部低纹理区域，并通过特征退化增强模块（FDEM）生成空间退化权重并调节特征增强强度。在基于DAWN和ACDC构建的雾天数据集上，Fog-RT-DETR的mAP@0.5和mAP@0.5∶0.95分别达到81.45%和58.78%，较基准模型分别提高2.12百分点和3.89百分点，召回率提高0.49百分点。消融实验表明，3个模块的联合使用取得了试验中的最佳检测结果；通道压缩试验进一步展示了模型在精度与计算开销之间的调整空间。

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