Article(id=1309639625426165955, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250418, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1758470400000, revisedDateStr=2025-09-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172018629, onlineDateStr=2026-09-23, pubDate=1779552000000, pubDateStr=2026-05-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172018629, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172018629, creator=13701087609, updateTime=1790172018629, 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=7, endPage=14, ext={EN=ArticleExt(id=1309639625631686852, articleId=1309639625426165955, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Road Surface Irregularity Recognition Method for Vehicles Based on YOLOv10, 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 issues of single-type detection and poor adaptability in detecting distant small targets and low-light scenes for road surface irregularity recognition, this paper proposes an improved YOLOv10-based detection algorithm. The SPPF module is replaced with a Focal Modulation network to enhance small-target detection accuracy. The CPA-Enhancer is introduced to improve detection performance under low-light conditions. The Shape-IoU loss function is adopted to optimize localization precision. Experimental results on a self-built dataset demonstrate that the improved model achieves a 3.08% higher mAP@0.5 than the original model. Real-vehicle deployment further verifies the effectiveness of the proposed model, demonstrating its capability for real-time detection of 7 types of road surface irregularities, including speed bumps, potholes, and vibrating markings.

, authors=Weiqiang Wang, Zhangtao He, Peng Zhou, Yiming He, Hongwen Bao, authorsList=Weiqiang Wang, Zhangtao He, Peng Zhou, Yiming He, Hongwen Bao, 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=1309639626072088773, articleId=1309639625426165955, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于YOLOv10的汽车路面不平特征识别方法研究, 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=

针对现有路面不平特征识别类型单一、对远小目标及低照度场景适应性差等问题，提出一种基于改进YOLOv10的检测算法。利用焦点调制网络替换快速空间金字塔池化（SPPF）模块，提升小目标检测精度；引入CPA-Enhancer架构增强低照度环境检测效果；采用形状交并比损失函数优化定位精度。经自建数据集试验结果表明：改进模型的mAP@0.5较原模型提升3.08%，通过实车部署验证其有效性，可实现减速带、凹坑、振动标线等7类路面不平特征的实时检测。

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