Article(id=1321893301876273643, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, 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=1765814400000, receivedDateStr=2025-12-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093522756, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093522756, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093522756, creator=13701087609, updateTime=1793093522756, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=183, endPage=190, ext={EN=ArticleExt(id=1321893303352668652, articleId=1321893301876273643, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Coal Quantity Detection Method of Belt Conveyor Based on LiDAR and Visual Image Fusion, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems of insufficient accuracy and weak environmental adaptability of traditional coal quantity detection devices due to the dynamic change of coal volume and the harsh working conditions such as high dust and high water mist in coal mines, a coal quantity detection method based on LiDAR and visual image fusion for extreme working conditions was proposed. Firstly, the joint calibration of LiDAR and visual camera was carried out, the mapping relationship was established and the coordinate system was unified. Then, the sampling data was obtained by a specific strategy, and the coal region was identified by Poisson reconstruction algorithm, spatial filtering strategy and structural similarity index. Finally, the depth image completion network was used to repair the depth map, and the coal amount was calculated based on the repair results. The results show that the maximum detection error of the fusion method of LiDAR and visual camera is only 2.90%, and the error decreases further with the increase of coal volume. Compared with mainstream depth completion algorithms such as ENet, the proposed algorithm achieves the best core accuracy index and meets the industrial real-time requirements. This method is basically consistent with the fluctuation of the detection curve of the contact electronic belt scale, which verifies its reliability. This method effectively overcomes the limitations of a single sensor, significantly improves the accuracy of coal quantity detection, and can operate stably in harsh environments. It provides a new technical solution for the accurate detection of coal quantity of belt conveyor and holds great significance for promoting the intelligent and efficient production of coal mines.

, authors=Jinhui Liu, Zhenxiang Yang, Hao Wang, Dongdong Ren, authorsList=Jinhui Liu, Zhenxiang Yang, Hao Wang, Dongdong Ren, authorCompany=null, correspAuthors=Zhenxiang Yang, 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=1321893304006980077, articleId=1321893301876273643, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于激光雷达与视觉图像融合的带式输送机煤量检测方法研究, 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=

针对煤料体积动态变化及煤矿井下高粉尘、高水雾等恶劣工况，导致传统煤量检测装置精度不足、环境适应性弱等问题，提出了一种面向极端工况的激光雷达与视觉图像融合煤量检测方法。首先，对激光雷达与视觉相机进行联合标定，建立映射关系并统一坐标系；然后，通过特定策略获取采样数据，利用泊松重建算法、空间过滤策略和结构相似性指数识别煤料区域；最后，运用深度图像补全网络修复深度图，并基于修复结果计算煤量。试验结果表明，激光雷达与视觉相机融合方法的最大检测误差仅为2.90%，且随着煤料体积的增大，误差进一步降低；与ENet等主流深度补全算法对比，所提算法核心精度指标最优，且能满足工业实时性要求；该方法与接触式电子皮带秤的检测曲线波动基本一致，验证了其可靠性。该方法有效克服了单一传感器的局限性，显著提高了煤量检测精度，并能在恶劣环境下稳定运行，为带式输送机煤量精准检测提供了新的技术方案，对推动煤矿智能化高效生产具有重要意义。

, authors=柳金晖, 杨祯祥, 王浩, 任栋栋, authorsList=柳金晖, 杨祯祥, 王浩, 任栋栋, authorCompany=null, correspAuthors=杨祯祥, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=aC4SdmUK39rKhh+xRtCY3g==, magXml=o9pNDETMogNAdR9f3AQLzQ==, pdfUrl=null, pdf=fFKlP0VS/F2RZiemH9RalQ==, pdfFileSize=3201528, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=BtGHwP64UnMfVpnQ5E45/g==, mapNumber=null, fund=null)}, authors=[Author(id=1321893304501907954, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893301876273643, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=928626070@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893304615154164, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893301876273643, authorId=1321893304501907954, language=EN, stringName=Jinhui Liu, firstName=Jinhui, middleName=null, lastName=Liu, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Huating Coal Industry Group Co., Ltd., Huating, Gansu 744103, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893304736788981, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893301876273643, authorId=1321893304501907954, language=CN, stringName=柳金晖, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=华亭煤业集团有限责任公司，甘肃 华亭市　744103, bio={"content":"

柳金晖（1983—），男，甘肃庄浪人，高级工程师，主要从事矿山机械方面的研究。E-mail:928626070@qq.com

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柳金晖（1983—），男，甘肃庄浪人，高级工程师，主要从事矿山机械方面的研究。E-mail:928626070@qq.com

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