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Article(id=1321893368783814816, 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=1754841600000, receivedDateStr=2025-08-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093538709, onlineDateStr=2026-10-27, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093538709, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093538709, creator=13701087609, updateTime=1793093538709, 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=232, endPage=241, ext={EN=ArticleExt(id=1321893368934809761, articleId=1321893368783814816, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Detection Algorithm for Coal Mine Hydraulic Support Protection Plates Based on Improved YOLOv8n, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the problems of low detection accuracy and severe missed detections of small targets for hydraulic support protection plates caused by the complex underground environments of coal mines, an improved YOLOv8n detection algorithm (CMH-YOLO) was proposed. In the CMH-YOLO algorithm, a SPDConv space-to-depth convolution module was first introduced into the shallow network to encode spatial information into the channel dimension, thereby preserving the detailed features of small targets. Subsequently, a lightweight C2f_MSA multi-scale attention module was designed, which integrated the long-range dependency modeling of large-kernel separable attention with the adaptive feature selection of efficient channel attention. This enabled the shallow network to simultaneously capture protection plate features at various distances and scales. To tackle the blurred surface features and partial occlusion of protection plates caused by the highly dusty underground environment, a C2f_DRFE dust-robust feature enhancement module was constructed. This module expanded the receptive field through multi-scale dilated convolutions to acquire information from surrounding unconcluded regions, and worked in tandem with deformable convolutions to adaptively avoid dense dust areas. Meanwhile, a BiFPN bidirectional feature pyramid network was introduced to replace the traditional PAN-FPN, which further improved the detection performance of the model. Finally, the Inner-IoU loss function was adopted, utilizing an auxiliary bounding box mechanism to enhance bounding box regression accuracy. Experimental validation was conducted on a coal mine hydraulic support dataset containing 20 045 annotated images. The results show that the mAP@0.5, mAP@0.5:0.95, precision, and recall of CMH-YOLO reach 97.9%, 81.4%, 93.8% and 94.5%, respectively. Compared with the baseline model YOLOv8n, these metrics are improved by 1.7, 1.8, 2.7, 1.6 percentage points, respectively, successfully maintaining the lightweight nature of the model while enhancing detection accuracy. The proposed algorithm effectively resolves issues such as small target recognition, multi-scale adaptation, and dust interference in extreme environments. It provides a reliable real-time monitoring solution for the safety monitoring of coal mine hydraulic supports, which holds significant engineering value for preventing roof accidents and ensuring miner safety.

, authors=Shi Luo1, 2, Wei Liang2, Yongan Li3, 4, Jiahao Li1, 2, authorsList=Shi Luo, Wei Liang, Yongan Li, Jiahao Li, authorCompany=null, correspAuthors=Yongan Li, 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=1321893369211633826, articleId=1321893368783814816, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于改进YOLOv8n的煤矿液压支架防护板检测算法, 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=

针对煤矿井下复杂环境导致液压支架防护板检测精度低、小目标漏检严重的问题，提出了一种改进YOLOv8n检测算法（CMH-YOLO）。CMH-YOLO算法首先在浅层网络引入SPDConv空间深度转换卷积模块，将空间信息编码到通道维度，以保留小目标细节特征。设计了轻量化C2f_MSA多尺度注意力模块，融合大核可分离注意力的长程依赖建模和高效通道注意力的自适应特征选择，使浅层网络能够同时捕获远近不同尺度的防护板特征。针对井下高粉尘环境造成防护板表面特征模糊和局部遮挡，构建了C2f_DRFE粉尘鲁棒特征增强模块，通过多尺度空洞卷积扩大感受野，从周围未遮挡区域获取信息，并配合可变形卷积自适应避开粉尘密集区域。同时，引入BiFPN双向特征金字塔网络替代传统PAN-FPN，进一步提升模型的检测性能。最后，采用Inner-IoU损失函数，利用辅助边界框机制提升边界框回归精度。在包含20 045张标注图像的煤矿液压支架数据集上进行了试验验证，CMH-YOLO算法的mAP@0.5达到97.9%、mAP@0.5：0.95达到81.4%、精确率达到93.8%，召回率达到94.5%，较基准模型YOLOv8n分别提升1.7个百分点、1.8个百分点、2.7个百分点和1.6个百分点，在提升检测精度的同时保持了模型轻量化。该算法有效解决了极端环境下小目标识别、多尺度适应与粉尘干扰等问题，为煤矿液压支架安全监测提供了可靠的实时监测方案，对预防顶板事故和保障矿工安全具有重要工程价值。

, authors=罗实1, 2, 梁威2, 李永安3, 4, 李佳浩1, 2, authorsList=罗实, 梁威, 李永安, 李佳浩, authorCompany=null, correspAuthors=李永安, authorNote=

罗实（2001—），男，四川德阳人，硕士研究生，主要从事图像识别、路径规划等方面的研究。E-mail:

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李永安（1984—），男，陕西杨凌人，副教授，主要从事煤矿先进掘、支、运机电装备电液控制技术、新型流控元件开发等领域的研究。E-mail:
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2．太原理工大学 山西省煤矿智能装备工程研究中心，山西 太原　030024, bio={"content":"

罗实（2001—），男，四川德阳人，硕士研究生，主要从事图像识别、路径规划等方面的研究。E-mail:

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罗实（2001—），男，四川德阳人，硕士研究生，主要从事图像识别、路径规划等方面的研究。E-mail:

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基于改进YOLOv8n的煤矿液压支架防护板检测算法
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罗实 1, 2 , 梁威 2 , 李永安 3, 4 , 李佳浩 1, 2
矿业研究与开发 | 矿山机电与矿业智能化 2026,46(7): 232-241
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矿业研究与开发 |矿山机电与矿业智能化 2026 , 46 (7) : 232 -241
基于改进YOLOv8n的煤矿液压支架防护板检测算法
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罗实（2001—），男，四川德阳人，硕士研究生，主要从事图像识别、路径规划等方面的研究。E-mail:

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罗实（2001—），男，四川德阳人，硕士研究生，主要从事图像识别、路径规划等方面的研究。E-mail:

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罗实1, 2 , 梁威2, 李永安3, 4 , 李佳浩1, 2
作者信息
  • 1．太原理工大学 安全与应急管理工程学院，山西 太原　030600
  • 2．太原理工大学 山西省煤矿智能装备工程研究中心，山西 太原　030024
  • 3．太原理工大学 机械工程学院，山西 太原　030024
  • 4．新疆智能装备研究院，新疆 阿克苏市　843000
通讯作者:
李永安（1984—），男，陕西杨凌人，副教授，主要从事煤矿先进掘、支、运机电装备电液控制技术、新型流控元件开发等领域的研究。E-mail:
作者简介:

罗实（2001—），男，四川德阳人，硕士研究生，主要从事图像识别、路径规划等方面的研究。E-mail:

Detection Algorithm for Coal Mine Hydraulic Support Protection Plates Based on Improved YOLOv8n
Shi Luo1, 2 , Wei Liang2, Yongan Li3, 4 , Jiahao Li1, 2
Affiliations
  • 1.College of Safety and Emergency Management and Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030600, China
  • 2.Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China
  • 3.College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China
  • 4.Xinjiang Intelligent Equipment Research Institute, Aksu, Xinjiang 843000, China
出版时间: 2026-07-25
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针对煤矿井下复杂环境导致液压支架防护板检测精度低、小目标漏检严重的问题，提出了一种改进YOLOv8n检测算法（CMH-YOLO）。CMH-YOLO算法首先在浅层网络引入SPDConv空间深度转换卷积模块，将空间信息编码到通道维度，以保留小目标细节特征。设计了轻量化C2f_MSA多尺度注意力模块，融合大核可分离注意力的长程依赖建模和高效通道注意力的自适应特征选择，使浅层网络能够同时捕获远近不同尺度的防护板特征。针对井下高粉尘环境造成防护板表面特征模糊和局部遮挡，构建了C2f_DRFE粉尘鲁棒特征增强模块，通过多尺度空洞卷积扩大感受野，从周围未遮挡区域获取信息，并配合可变形卷积自适应避开粉尘密集区域。同时，引入BiFPN双向特征金字塔网络替代传统PAN-FPN，进一步提升模型的检测性能。最后，采用Inner-IoU损失函数，利用辅助边界框机制提升边界框回归精度。在包含20 045张标注图像的煤矿液压支架数据集上进行了试验验证，CMH-YOLO算法的mAP@0.5达到97.9%、mAP@0.5：0.95达到81.4%、精确率达到93.8%，召回率达到94.5%，较基准模型YOLOv8n分别提升1.7个百分点、1.8个百分点、2.7个百分点和1.6个百分点，在提升检测精度的同时保持了模型轻量化。该算法有效解决了极端环境下小目标识别、多尺度适应与粉尘干扰等问题，为煤矿液压支架安全监测提供了可靠的实时监测方案，对预防顶板事故和保障矿工安全具有重要工程价值。

煤矿安全  /  液压支架  /  防护板检测  /  改进YOLOv8n  /  多尺度注意力  /  粉尘鲁棒特征

To address the problems of low detection accuracy and severe missed detections of small targets for hydraulic support protection plates caused by the complex underground environments of coal mines, an improved YOLOv8n detection algorithm (CMH-YOLO) was proposed. In the CMH-YOLO algorithm, a SPDConv space-to-depth convolution module was first introduced into the shallow network to encode spatial information into the channel dimension, thereby preserving the detailed features of small targets. Subsequently, a lightweight C2f_MSA multi-scale attention module was designed, which integrated the long-range dependency modeling of large-kernel separable attention with the adaptive feature selection of efficient channel attention. This enabled the shallow network to simultaneously capture protection plate features at various distances and scales. To tackle the blurred surface features and partial occlusion of protection plates caused by the highly dusty underground environment, a C2f_DRFE dust-robust feature enhancement module was constructed. This module expanded the receptive field through multi-scale dilated convolutions to acquire information from surrounding unconcluded regions, and worked in tandem with deformable convolutions to adaptively avoid dense dust areas. Meanwhile, a BiFPN bidirectional feature pyramid network was introduced to replace the traditional PAN-FPN, which further improved the detection performance of the model. Finally, the Inner-IoU loss function was adopted, utilizing an auxiliary bounding box mechanism to enhance bounding box regression accuracy. Experimental validation was conducted on a coal mine hydraulic support dataset containing 20 045 annotated images. The results show that the mAP@0.5, mAP@0.5:0.95, precision, and recall of CMH-YOLO reach 97.9%, 81.4%, 93.8% and 94.5%, respectively. Compared with the baseline model YOLOv8n, these metrics are improved by 1.7, 1.8, 2.7, 1.6 percentage points, respectively, successfully maintaining the lightweight nature of the model while enhancing detection accuracy. The proposed algorithm effectively resolves issues such as small target recognition, multi-scale adaptation, and dust interference in extreme environments. It provides a reliable real-time monitoring solution for the safety monitoring of coal mine hydraulic supports, which holds significant engineering value for preventing roof accidents and ensuring miner safety.

Coal mine safety  /  Hydraulic support  /  Protection plate detection  /  Improved YOLOv8n  /  Multi-scale attention  /  Dust-robust feature
罗实, 梁威, 李永安, 李佳浩. 基于改进YOLOv8n的煤矿液压支架防护板检测算法. 矿业研究与开发, 2026 , 46 (7) : 232 -241 .
Shi Luo, Wei Liang, Yongan Li, Jiahao Li. Detection Algorithm for Coal Mine Hydraulic Support Protection Plates Based on Improved YOLOv8n[J]. Mining Research and Development, 2026 , 46 (7) : 232 -241 .
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  • 山西省重点研发计划项目(202102100401017)
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  • 接收时间：2025-08-11
  • 首发时间：2026-10-27
  • 出版时间：2026-07-25
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  • 收稿日期：2025-08-11
基金
山西省重点研发计划项目(202102100401017)
作者信息
    1．太原理工大学 安全与应急管理工程学院，山西 太原　030600
    2．太原理工大学 山西省煤矿智能装备工程研究中心，山西 太原　030024
    3．太原理工大学 机械工程学院，山西 太原　030024
    4．新疆智能装备研究院，新疆 阿克苏市　843000

通讯作者:

李永安（1984—），男，陕西杨凌人，副教授，主要从事煤矿先进掘、支、运机电装备电液控制技术、新型流控元件开发等领域的研究。E-mail:
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