Article(id=1321893356645498930, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751385600000, receivedDateStr=2025-07-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093535815, onlineDateStr=2026-10-27, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093535815, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093535815, creator=13701087609, updateTime=1793093535815, updator=13701087609, issue=Issue{id=1321893331622286028, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='7', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093529849, creator='13701087609', updateTime=1793094587578, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897768357048353, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897768357048354, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=257, endPage=264, ext={EN=ArticleExt(id=1321893358432272435, articleId=1321893356645498930, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Intelligent Perception Technology and Application Demonstration for Unmanned Operations Under Dust Interference in Open-Pit Mines, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

During the transport of unmanned mining trucks in open-pit mines, their perception systems are highly susceptible to harsh weather conditions such as wind-blown sand and dust. This vulnerability leads to a degradation in the detection and tracking capabilities of key targets, consequently causing adverse consequences such as missed detections, false alarms, and unintended vehicle stops. To enhance the safety and efficiency of perception technologies for unmanned operations under dust interference, an intelligent dust perception model and scheme for unmanned mining trucks were proposed by integrating deep learning algorithms, point cloud data processing techniques and multi-sensor fusion methods, followed by experimental validations. On this basis, a classification strategy and corresponding criteria for dust severity during unmanned operations were established, and the trained model was evaluated on a test dataset. The results indicate that the predicted mean Intersection over Union reaches 87.3%, with the Intersection over Union for the dust category specifically achieving 90.7%. This demonstrates that the perception model possesses high recognition precision for dust, fully satisfying the requirements of dust scene recognition for unmanned operations in mining areas. The unmanned operation dust intelligent perception scheme effectively enhances vehicle perception capabilities in open-pit unmanned operation environments, achieving a point cloud removal rate of over 95%. It significantly improves the accuracy and sensitivity of environmental perception, thereby providing a robust guarantee for the safe operation of unmanned mining trucks.

, authors=Qiao Lin¹, Lei Zhang¹, Qingwei Kang², Yongsheng Gao³, Sihui Zeng⁴, Zhigao Liu¹, Shujie Yang¹, authorsList=Qiao Lin, Lei Zhang, Qingwei Kang, Yongsheng Gao, Sihui Zeng, Zhigao Liu, Shujie 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=1321893358671347764, articleId=1321893356645498930, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=露天矿山扬尘影响下无人驾驶智能感知技术及应用示范, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

露天矿山无人驾驶矿卡在运输过程中，其感知系统易受风沙、扬尘等恶劣天气影响，导致关键目标的检测与跟踪能力下降，进而引发目标漏检、误检以及停车等后果。为提升扬尘影响下无人驾驶安全高效感知技术水平，采用深度学习算法、点云数据处理技术及多传感器融合方法，提出了无人驾驶矿卡扬尘智能感知模型及方案，并开展了试验验证。在此基础上，确定了无人驾驶运行扬尘分级策略和依据，并将训练好的模型在测试集上进行评估。结果表明，预测平均交并比指标达到87.3%，其中灰尘类别的交并比达到了90.7%，表明感知模型对灰尘类别具有较高的识别精度，能够充分满足矿区无人驾驶对扬尘场景识别的需求。无人驾驶扬尘智能感知方案有效地提升了露天矿无人驾驶环境下的车辆感知能力，点云去除率达95%以上，提高了对环境的感知准确度和敏感度，为无人驾驶矿卡的安全运营提供了有力保障。

, authors=林巧¹, 张磊¹, 康庆微², 高永胜³, 曾思慧⁴, 刘志高¹, 杨书杰¹, authorsList=林巧, 张磊, 康庆微, 高永胜, 曾思慧, 刘志高, 杨书杰, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=IWsgr07LrzdPTDvrYqcNdg==, magXml=UqHrCedn7p3E3rUxVAQTpA==, pdfUrl=null, pdf=u2D0i2lYcGn+JjKcyh9rVQ==, pdfFileSize=2960120, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=19tyV8odPpqyNHGzpwJBRQ==, mapNumber=null, fund=null)}, authors=[Author(id=1321893359229190210, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893356645498930, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=linqiao@eacon.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893359325659204, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893356645498930, authorId=1321893359229190210, language=EN, stringName=Qiao Lin, firstName=Qiao, middleName=null, lastName=Lin, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.EACON Group Co., Ltd., Beijing 100083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893359409545285, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893356645498930, authorId=1321893359229190210, language=CN, stringName=林巧, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．易控智驾科技股份有限公司，北京　100083, bio={"content":"

林巧（1984—），男，浙江温州人，博士，高级工程师，主要从事露天矿山无人驾驶研究。E-mail:linqiao@eacon.com

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林巧（1984—），男，浙江温州人，博士，高级工程师，主要从事露天矿山无人驾驶研究。E-mail:linqiao@eacon.com

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