Article(id=1281683192608441035, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.05.1654, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733760000000, receivedDateStr=2024-12-10, revisedDate=1739376000000, revisedDateStr=2025-02-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1783506685639, onlineDateStr=2026-07-08, pubDate=1748361600000, pubDateStr=2025-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783506685639, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783506685639, creator=13701087609, updateTime=1783506685639, updator=13701087609, issue=Issue{id=1281683189131359212, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='5', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1748361600000', pubDateStr='2025-05-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783506684811, creator='13701087609', updateTime=1783589333681, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282029843684504545, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282029843684504546, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=195, endPage=203, ext={EN=ArticleExt(id=1281683193002705612, articleId=1281683192608441035, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Multimodal fusion-based obstacle detection in low-visibility open-pit mines, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To address the perception inaccuracies of autonomous mining trucks in open-pit mines under low-visibility and low-illumination conditions—issues that may lead to obstacle collisions. This paper was proposed a multimodal fusion-based obstacle detection method to enhance detection accuracy and operational safety in complex environments. Firstly, local Feature matching at light speed (LightGlue), was employed to achieve spatial alignment between thermal infrared and visible light images, thereby avoiding spatial misalignment and geometric distortion prior to fusion. Secondly, in the modality feature extraction and fusion stage, a Dual-Modality Feature Fusion (DMFF) module was incorporated into the improved dual-branch backbone network. The extraction capability of dual-modality features was enhanced and fusion was performed through feature compression and cross-modal feature enhancement. An iterative learning method was then introduced to effectively match the complementary information between modalities, generating a fused dual-modality feature map and improving multimodal detection performance. Finally, the fused feature maps at multiple scales were input into the detection head. They were combined with bounding box regression and classification prediction for precise detection. Experimental results demonstrate that the proposed method achieves excellent obstacle detection performance in challenging scenarios with low visibility. Specifically, it achieves a mean Average Precision (mAP@0.5) of 90.8%, and an F₁-score of 0.887, outperforming existing methods in both accuracy and speed. Moreover, the proposed approach exhibits lower false positive and miss detection rates, effectively ensuring the safe navigation of autonomous mining trucks in complex operational environments.

, authors=Fengzhan YANG^{1, 2}, Qinghua GU^{1, 2}, Shaobo LI^{1, 2}, Jianchun YANG³, authorsList=Fengzhan YANG, Qinghua GU, Shaobo LI, Jianchun YANG, 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=1281683195548648143, articleId=1281683192608441035, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=露天矿低能见度下多模态融合障碍物检测, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为解决露天矿区低能见度、低照度环境中无人矿卡对行进障碍物的感知误差问题，减少碰撞风险，提出基于多模态融合的障碍物检测方法；首先，采用轻量粘合（LightGlue）图像配准算法，对齐热红外与可见光不同模态图像的空间，避免融合前的空间错位与几何失真；其次，模态特征提取与融合阶段，在改进的双分支主干网络引入双模态特征融合（DMFF）模块，通过特征压缩、跨模态特征增强，提高提取双模态特征的能力并完成融合；然后，引入迭代学习方法，深入匹配模态间的互补信息，获取双模态特征映射图，提高多模态检测性能；最后，将各尺度融合的特征映射图输入到检测头部，结合边界框回归与分类预测进行精确检测。结果表明：该方法在低能见度等复杂场景下对障碍物的检测效果较好，阈值为0.5时的平均精度均值（mAP@0.5）达到90.8%；F₁平衡分数达到0.887，较现有方法表现出更高精度和速度，并且误报率和漏检率也较低，能有效提升无人矿卡在复杂环境下的检测精度和安全性。

, authors=杨奉展^{1, 2}, 顾清华教授^{1, 2}, 李少博^{1, 2}, 杨建春工程师³, authorsList=杨奉展, 顾清华教授, 李少博, 杨建春工程师, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=8m+I9Ju2MAYKU5yYqxwS0Q==, magXml=wBRu1YpWb0AwFOAaY/lZ7w==, pdfUrl=null, pdf=9AvyhFo88KeAyCypatZTUg==, pdfFileSize=8944934, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=avwAvVDXUMtkn6IEuhbSSw==, mapNumber=null, fund=null)}, authors=[Author(id=1281683196144239322, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683192608441035, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=19854179563@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281683196341371613, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683192608441035, authorId=1281683196144239322, language=EN, stringName=Fengzhan YANG, firstName=Fengzhan, middleName=null, lastName=YANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹School of Resources Eneineering, Xi'an Universinv of Architecure and Technology, Xi'an Shaanxi 710055, China
²Xi'an Key Laboratory of Intelligent Industry Perception Computing and Decision Making, Xi'an Universily of Architecture and Technology, Xi'an Shaanxi 710055, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281683196433646302, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683192608441035, authorId=1281683196144239322, language=CN, stringName=杨奉展, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹西安建筑科技大学 资源工程学院，陕西 西安 710055
²西安建筑科技大学 西安市智慧工业感知计算与决策重点实验室，陕西 西安 710055, bio={"content":"

杨奉展（2001—），男，山东淄博人，硕士研究生，研究方向为矿山无人驾驶感知预警。E-mail:19854179563@163.com。

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杨奉展（2001—），男，山东淄博人，硕士研究生，研究方向为矿山无人驾驶感知预警。E-mail:19854179563@163.com。

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