Article(id=1309639173259223059, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250865, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1772985600000, revisedDateStr=2026-03-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171910823, onlineDateStr=2026-09-23, pubDate=1784822400000, pubDateStr=2026-07-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171910823, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171910823, creator=13701087609, updateTime=1790171910823, updator=13701087609, issue=Issue{id=1309639160835698887, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='7', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1784822400000', pubDateStr='2026-07-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171907861, creator='13701087609', updateTime=1790232758449, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894386750681363, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894386750681364, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=10, ext={EN=ArticleExt(id=1309639173460549652, articleId=1309639173259223059, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=A Multi-Scale Vehicle Detection Algorithm for Autonomous Driving Scenarios, columnId=1309639161502593225, journalTitle=Automobile Technology, columnName=Special Topic on Object Detection and Road Lane Recognition Technologies for Autonomous Driving, runingTitle=null, highlight=null, articleAbstract=

To address challenges in autonomous driving scenarios, such as uneven distribution of target scales, significant background interference, structural distortion of small objects, and high computational complexity, this paper proposes an improved algorithm, ELD-YOLO, based on YOLOv11n. The C3k2-EMSC module is introduced to enhance multi-scale feature extraction, capturing richer contextual information in complex traffic scenarios. The LSDECD module is designed to strengthen feature representation through shared convolutions and group normalization, reducing redundant responses. The DySample module is introduced to enhance small object and edge feature representation through dynamic sampling. Results demonstrate that on the KITTI and BDD100K datasets, the parameter quantity of ELD-YOLO is reduced by 16% compared with YOLOv11n, while achieving mAP@0.5s of 89.7% and 52.7%, respectively. This approach significantly improves detection accuracy and robustness while reducing model complexity.

, authors=Yaluo Zhou^{1, 3}, Haiming Wang^{1, 3}, Wenguang Liu², Ruicheng Zhang^{1, 3}, authorsList=Yaluo Zhou, Haiming Wang, Wenguang Liu, Ruicheng Zhang, 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=1309639173708013589, articleId=1309639173259223059, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=面向自动驾驶场景的多尺度车辆检测算法, columnId=1309639161645199563, journalTitle=汽车技术, columnName=面向智能驾驶的目标检测与道路通行区域感知技术专题, runingTitle=null, highlight=null, articleAbstract=

针对自动驾驶交通场景中车辆检测目标尺度分布不均、背景干扰显著、小目标结构失真以及计算复杂度高等问题，提出一种基于YOLOv11n的改进算法ELD-YOLO算法。通过引入C3k2-EMSC模块，增强多尺度特征提取能力，在复杂交通场景下获得更丰富的上下文信息；LSDECD模块利用共享卷积与群归一化强化特征表达，减少冗余响应；通过动态采样（DySample）提升小目标与边缘特征表征能力。试验结果表明：在KITTI与BDD100K数据集上，ELD-YOLO参数量较YOLOv11n降低16%，mAP@0.5分别达到89.7%和52.7%，降低模型复杂度的同时，显著提升检测精度与鲁棒性。

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