Article(id=1314907900879921480, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2026.02.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760284800000, receivedDateStr=2025-10-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791428073377, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791428073377, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791428073377, creator=13701087609, updateTime=1791428073377, updator=13701087609, issue=Issue{id=1314907832038810534, tenantId=1146029695717560320, journalId=1235980550691926019, year='2026', volume='46', issue='2', pageStart='1', pageEnd='214', issueExtLink='null', onlineDate='null', pubDate='1774972800000', pubDateStr='2026-04-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791428056964, creator='13701087609', updateTime=1791430468655, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314917947500486818, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314917947500486819, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=28, endPage=35, ext={EN=ArticleExt(id=1314907902876410188, articleId=1314907900879921480, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Research and Application of Dynamic Recognition Model for Blast Fragmentation in Open-Pit Mines, columnId=1236276106018484431, journalTitle=Mining and Metallurgical Engineering, columnName=MINING, runingTitle=null, highlight=null, articleAbstract=

For rapid and automatic recognition of blast fragmentation in the Husab Mine in Namibia of Africa, a dynamic recognition model was proposed for blast fragmentation in open-pit mines. By acquiring stereo images through a binocular camera and constructing a depth map, in combination with an improved YOLOv8 instance segmentation model, the classification of gravel and fragmentation analysis were realized. Moreover, transfer learning, ROI extraction, and shadow data augmentation technologies were all adopted to optimize the model, enhancing its recognition accuracy in complex environments. The model training results show that the optimized model can achieve a precision of 0.375, a recall of 0.600, and an accuracy of 0.300. This model exhibits strong robustness in extremely complex scenes of open-pit mines, and is capable of successfully detecting most effective rock blocks from high-noise and high-interference backgrounds. Field measurement data indicate that the model can operate stably in environments with high dust and dynamic lighting, achieving a recognition accuracy of 85%for effective rocks on site, and the error of the fragmentation distribution curve is controlled within 8%. This model provides reliable technical support for the efficient evaluation and intelligent detection of blasting effects in the Husab Mine.

, authors=Lei HUANG1, Ming TAO2, Yulong LIU1, Zhao LUO2, Yuanquan XU2, authorsList=Lei HUANG, Ming TAO, Yulong LIU, Zhao LUO, Yuanquan XU, authorCompany=null, correspAuthors=Zhao LUO, 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=1314907903442641229, articleId=1314907900879921480, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=露天矿爆破块度动态识别模型研究与应用, columnId=1236276106219811026, journalTitle=矿冶工程杂志, columnName=采矿, runingTitle=null, highlight=null, articleAbstract=

为解决非洲纳米比亚湖山矿爆破块度快速自动识别的问题,提出了一种露天矿爆破块度动态识别模型,通过双目相机获取立体图像并构建深度图,结合改进的YOLOv8实例分割模型实现碎石分类与块度分析;并采用迁移学习、ROI提取和阴影数据增强技术优化模型,提升模型在复杂环境下的识别精度。模型训练结果表明,优化后的模型精确率达0.375,召回率0.600,准确度0.300,该模型在露天矿极端复杂场景下具有强大鲁棒性,能从高噪、高干扰的背景中成功检出大部分有效岩块。现场实测数据表明,该模型能在高粉尘与动态光照环境下稳定运行,对现场有效岩石的识别准确率达85%,块度分布曲线误差控制在8%以内。该模型为湖山矿爆破效果的高效评估与智能检测提供了可靠的技术支撑。

, authors=黄磊1, 陶明2, 刘玉龙1, 罗朝2, 徐源泉2, authorsList=黄磊, 陶明, 刘玉龙, 罗朝, 徐源泉, authorCompany=null, correspAuthors=罗朝, authorNote=

黄磊(1987-),男,四川内江人,硕士,高级工程师,主要从事露天采矿、工程爆破技术经济等工作。E-mail:

, correspAuthorsNote=
罗朝(2003-),男,湖南邵阳人,硕士研究生,主要从事矿山爆破、智慧矿山等研究工作。E-mail:
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黄磊(1987-),男,四川内江人,硕士,高级工程师,主要从事露天采矿、工程爆破技术经济等工作。E-mail:

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黄磊(1987-),男,四川内江人,硕士,高级工程师,主要从事露天采矿、工程爆破技术经济等工作。E-mail:

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露天矿爆破块度动态识别模型研究与应用
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黄磊 1 , 陶明 2 , 刘玉龙 1 , 罗朝 2 , 徐源泉 2
矿冶工程杂志 | 采矿 2026,46(2): 28-35
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矿冶工程杂志 |采矿 2026 , 46 (2) : 28 -35
露天矿爆破块度动态识别模型研究与应用
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黄磊(1987-),男,四川内江人,硕士,高级工程师,主要从事露天采矿、工程爆破技术经济等工作。E-mail:

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黄磊(1987-),男,四川内江人,硕士,高级工程师,主要从事露天采矿、工程爆破技术经济等工作。E-mail:

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黄磊1 , 陶明2, 刘玉龙1, 罗朝2 , 徐源泉2
作者信息
  • 1.中广核铀业发展有限公司,北京 100048
  • 2.中南大学 资源与安全工程学院,湖南 长沙 410083
通讯作者:
罗朝(2003-),男,湖南邵阳人,硕士研究生,主要从事矿山爆破、智慧矿山等研究工作。E-mail:
作者简介:

黄磊(1987-),男,四川内江人,硕士,高级工程师,主要从事露天采矿、工程爆破技术经济等工作。E-mail:

Research and Application of Dynamic Recognition Model for Blast Fragmentation in Open-Pit Mines
Lei HUANG1 , Ming TAO2, Yulong LIU1, Zhao LUO2 , Yuanquan XU2
Affiliations
  • 1.CGNPC Uranium Resources Co., Ltd., Beijing 100048, China
  • 2.School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
doi: 10.3969/j.issn.0253-6099.2026.02.005
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为解决非洲纳米比亚湖山矿爆破块度快速自动识别的问题,提出了一种露天矿爆破块度动态识别模型,通过双目相机获取立体图像并构建深度图,结合改进的YOLOv8实例分割模型实现碎石分类与块度分析;并采用迁移学习、ROI提取和阴影数据增强技术优化模型,提升模型在复杂环境下的识别精度。模型训练结果表明,优化后的模型精确率达0.375,召回率0.600,准确度0.300,该模型在露天矿极端复杂场景下具有强大鲁棒性,能从高噪、高干扰的背景中成功检出大部分有效岩块。现场实测数据表明,该模型能在高粉尘与动态光照环境下稳定运行,对现场有效岩石的识别准确率达85%,块度分布曲线误差控制在8%以内。该模型为湖山矿爆破效果的高效评估与智能检测提供了可靠的技术支撑。

爆破块度  /  图像识别  /  图像分割  /  YOLOv8  /  双目视觉  /  数据增强  /  ROI提取  /  迁移学习

For rapid and automatic recognition of blast fragmentation in the Husab Mine in Namibia of Africa, a dynamic recognition model was proposed for blast fragmentation in open-pit mines. By acquiring stereo images through a binocular camera and constructing a depth map, in combination with an improved YOLOv8 instance segmentation model, the classification of gravel and fragmentation analysis were realized. Moreover, transfer learning, ROI extraction, and shadow data augmentation technologies were all adopted to optimize the model, enhancing its recognition accuracy in complex environments. The model training results show that the optimized model can achieve a precision of 0.375, a recall of 0.600, and an accuracy of 0.300. This model exhibits strong robustness in extremely complex scenes of open-pit mines, and is capable of successfully detecting most effective rock blocks from high-noise and high-interference backgrounds. Field measurement data indicate that the model can operate stably in environments with high dust and dynamic lighting, achieving a recognition accuracy of 85%for effective rocks on site, and the error of the fragmentation distribution curve is controlled within 8%. This model provides reliable technical support for the efficient evaluation and intelligent detection of blasting effects in the Husab Mine.

blast fragmentation  /  image recognition  /  image segmentation  /  YOLOv8  /  binocular vision  /  data augmentation  /  ROI extraction  /  transfer learning
黄磊, 陶明, 刘玉龙, 罗朝, 徐源泉. 露天矿爆破块度动态识别模型研究与应用. 矿冶工程杂志, 2026 , 46 (2) : 28 -35 . DOI: 10.3969/j.issn.0253-6099.2026.02.005
Lei HUANG, Ming TAO, Yulong LIU, Zhao LUO, Yuanquan XU. Research and Application of Dynamic Recognition Model for Blast Fragmentation in Open-Pit Mines[J]. Mining and Metallurgical Engineering, 2026 , 46 (2) : 28 -35 . DOI: 10.3969/j.issn.0253-6099.2026.02.005
  • 国家自然科学基金面上项目(52274105)
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doi: 10.3969/j.issn.0253-6099.2026.02.005
  • 接收时间:2025-10-13
  • 首发时间:2026-10-08
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  • 收稿日期:2025-10-13
基金
国家自然科学基金面上项目(52274105)
作者信息
    1.中广核铀业发展有限公司,北京 100048
    2.中南大学 资源与安全工程学院,湖南 长沙 410083

通讯作者:

罗朝(2003-),男,湖南邵阳人,硕士研究生,主要从事矿山爆破、智慧矿山等研究工作。E-mail:
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2种不同金属材料的力学参数
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属
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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