Article(id=1309639169954115855, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250282, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1780502400000, revisedDateStr=2026-06-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171910036, onlineDateStr=2026-09-23, pubDate=1784822400000, pubDateStr=2026-07-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171910036, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171910036, creator=13701087609, updateTime=1790171910036, 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=20, endPage=27, ext={EN=ArticleExt(id=1309639170138665232, articleId=1309639169954115855, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Improvement of Traffic Guidance Device Detection Algorithm for Automatic Driving, 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 the challenge of balancing accuracy and real-time performance when autonomous vehicles detect traffic diversion devices in roadworks scenarios, a detection algorithm that improves upon YOLOv8 is proposed. This paper utiliseS Make Sense for data annotation and combined this with data augmentation techniques to construct a dedicated dataset. The backbone network with EfficientNetV2-S to enhance feature extraction is replaced and an inverted Residual Moving Block (iRMB) module before the Spatial Pyramid Pooling Fast (SPPF) module to focus on key targets is edded. Morever, the Distance-based Intersection-over-Union (DIou) loss function is adopted to optimise the model. Experimental results validate that the improved model achieves a precision of 90.7%, with a recall increase of 4.8 percentage points and an average precision (mAP@0.5 and mAP@0.5∶0.95) that has risen by 4.3 percentage points. Upon deployment to an Android mobile device, the frame rate remains stable at over 40 frames per second.

, authors=Ying Fan¹, Runyin Gu¹, Haichao An¹, Kun Zhi¹, Jianhua Tang², authorsList=Ying Fan, Runyin Gu, Haichao An, Kun Zhi, Jianhua Tang, 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=1309639170352574737, articleId=1309639169954115855, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=面向自动驾驶的交通导改装置检测算法, 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=

针对自动驾驶车辆在道路施工场景中对交通导改装置检测时面临的精度与实时性协同问题，提出一种改进的YOLOv8检测算法。利用Make Sense进行数据标注并结合数据增强技术构建专用数据集；以EfficientNetV2-S替换主干网络强化特征提取；在快速空间金字塔池化（SPPF）模块前增设反向残差注意力模块（iRMB）聚焦关键目标；采用距离交并比（DIoU）损失函数优化模型。经试验验证，改进后模型的精确率达90.7%，召回率提升4.8百分点，平均精度均值（mAP@0.5和mAP@0.5∶0.95）均提升4.3百分点。部署至Android移动端后，帧率稳定达40帧/s以上。

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