Article(id=1309639629515617029, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639624411152909, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20260019, 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=1790172019603, onlineDateStr=2026-09-23, pubDate=1779552000000, pubDateStr=2026-05-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172019603, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172019603, creator=13701087609, updateTime=1790172019603, 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=15, endPage=22, ext={EN=ArticleExt(id=1309639629725332230, articleId=1309639629515617029, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Multi-View Image Object Detection Algorithm Based on Improved BEVFormer, columnId=1309639624981578254, journalTitle=Automobile Technology, columnName=Special Topic on Full-Stack Environment Perception Technology for Intelligent Vehicles, runingTitle=null, highlight=null, articleAbstract=

To improve the accuracy and robustness of 3D object detection from a Bird's-Eye View (BEV) in multi-camera visual perception for autonomous driving, this paper proposes a multi-view image object detection method based on an improved BEVFormer. To introduce perspective-based regional auxiliary supervision at the image feature level, using BEV annotations to impose projection constraints on the image view, thereby guiding the backbone network to learn fine-grained local features for 3D spatial localization. This paper establishes instance-level consistency constraints, at the BEV representation level and explicitly enforces the consistency of BEV features for the same physical object across different viewpoints based on a contrastive learning mechanism, thereby mitigating instance-level feature drift caused by view transformations. Furthermore, this paper trains and evaluates the proposed method using the nuScenes dataset and conduct a comparative analysis with various typical multi-view 3D object detection algorithms. The experimental results show that, compared to the BEVFormer baseline model, the proposed method achieves improvements of 1.6 percentage points and 0.4 percentage points in mean Average Precision (mAP) and the Normalized Detection Score (NDS), respectively, while reducing the average localization error by 8.7 percentage points, thereby validating the effectiveness of this method for the BEV detection task using multi-view images.

, authors=Ziwei Li, Zihao Ai, authorsList=Ziwei Li, Zihao Ai, 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=1309639629943436039, articleId=1309639629515617029, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进BEVFormer的多视角图像目标检测算法, 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=

为了提升自动驾驶多摄像头视觉感知中鸟瞰视角（BEV）三维目标检测的精度与鲁棒性，提出一种基于改进BEVFormer的多视角图像目标检测方法。在图像特征层引入透视区域级辅助监督，通过BEV标注图像视角下的投影约束，引导骨干网络学习三维空间定位的局部细粒度特征；在BEV表征层构建实例级一致性约束，基于对比学习机制显式约束同一物理目标在不同视角条件下的BEV特征一致性，缓解视图变换引发的实例级特征漂移。基于nuScenes数据集对所提出方法进行训练与评估，并与多种典型多视角图像三维目标检测算法进行对比分析。试验结果表明：相较于BEVFormer基线模型，所提出方法在平均精度（mAP）和综合检测得分（NDS）指标上分别提升1.6百分点和0.4百分点，平均定位误差下降8.7百分点，验证了该方法在多视角图像BEV感知任务中的有效性。

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