Article(id=1309639999105102449, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250438, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1772035200000, revisedDateStr=2026-02-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172107721, onlineDateStr=2026-09-23, pubDate=1787500800000, pubDateStr=2026-08-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172107721, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172107721, creator=13701087609, updateTime=1790172107721, updator=13701087609, issue=Issue{id=1309639998379491369, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='8', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1787500800000', pubDateStr='2026-08-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172107547, creator='13701087609', updateTime=1790232702427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894151819330406, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894151819330407, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=12, endPage=18, ext={EN=ArticleExt(id=1309639999272874610, articleId=1309639999105102449, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Small Target Recognition Algorithm Based on Improved RT-DETR Network, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issues of insufficient accuracy and limited speed of the RT-DETR (real-time detection transformer) network in complex scenarios and small object detection, an improved detection algorithm for vehicle images is proposed. This algorithm enhances the ability to capture features of small-scale objects such as vehicles and pedestrians by introducing spatial positional relationship modeling and improves the network's nonlinear modeling performance through a gating mechanism. At the same time, by separating high-frequency and low-frequency detail information and combining feature map segmentation and stitching operations, feature sampling is achieved without information loss. By integrating convolution units (Conv) and batch normalization units (BN) to reduce intermediate processing steps and memory consumption, computational efficiency is increased and model performance is optimized. Experimental results show that compared to the original RT-DETR network, the improved algorithm increases mAP50, mAP50∶95, precision, and recall by 6%, 4.5%, 4.2%, and 4%, respectively. Therefore, optimizing the backbone network structure and strengthening multi-scale feature fusion, focusing on fine-grained information and spatial-frequency domain features, is an effective strategy to improve vehicle detection performance in complex environments.

, authors=Jiangong Hu¹, Xuyang Bu^{2, 3}, Wanli Gao³, Quanwei Wang², Jin Qin³, authorsList=Jiangong Hu, Xuyang Bu, Wanli Gao, Quanwei Wang, Jin Qin, authorCompany=null, correspAuthors=Xuyang Bu, 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=1309639999486784115, articleId=1309639999105102449, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进RT-DETR网络的小目标识别算法研究, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对RT-DETR（Real-Time Detection Transformer）网络在复杂场景与小目标检测中存在的精度不足与速度受限问题，提出了一种面向车辆图像的改进检测算法。该算法通过引入空间位置关系建模，增强对车辆、行人等小尺度目标的特征捕获能力，并采用门控机制提升网络的非线性建模性能。同时，通过分离高频与低频细节信息，结合特征图的分割与拼接操作，实现了无信息丢失的特征采样。通过将卷积单元Conv和批量归一化单元BN融合处理来减少中间处理环节和内存消耗，提高计算效率和优化模型性能。试验结果表明，相较于原始RT-DETR网络，改进算法在mAP50、mAP50∶95、精度及召回率上分别提升了6%、4.5%、4.2%与4%。因此，优化主干网络结构并强化多尺度特征融合，聚焦细粒度信息与空频域特征，是提升复杂环境下车辆检测性能的有效策略。

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