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露天矿无人机巡检: 轻量化目标检测与 多目标跟踪技术及系统构建.

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Title: 露天矿无人机巡检: 轻量化目标检测与 多目标跟踪技术及系统构建.
Alternate Title: UAV inspection in open-pit mine: lightweight target detection and multi-target tracking technology and system construction.
Authors: 刘光伟1 liuguangwei@lntu.edu.cn, 雷 健1 jianl2025@163.com, 张 磊1,2, 罗 霄3,4, 牛喜洋1, 张浩博1, 袁 杰1, 秦飞龙5,6, 谯乾林7
Source: Coal Science & Technology (0253-2336). Feb2026, Vol. 54 Issue 2, p462-481. 20p.
Subject Terms: *Multiple target tracking, *Drone surveillance, *Mines & mineral resources, *Signal detection, *Convolutional neural networks
Abstract (English): In order to supplement the limitations of active positioning technologies such as UWB indoor and outdoor positioning and vehicle-mounted strap-on inertial navigation in signal blind spots and non-cooperative target monitoring, this paper proposes lightweight target detection and multi-object tracking algorithms, and constructs an intelligent UAV inspection system. In the design of the detection model, the deformable convolutional DCNv2 and AFPN are introduced in the backbone network, and the progressive feature pyramid network AFPN is used to strengthen the multi-scale feature extraction ability. The lightweight detection head LSDECD-Head is designed to improve the detection accuracy of small and occluded targets combined with the Focaler-GIoU loss function. The model compression is realized by the LAMP pruning algorithm, and the performance of mAP50 of 0.868 and inference time of 196 ms is still maintained at 30% pruning rate, which adapts to the constraints of UAV computing resources. In terms of multi-object tracking, the ByteTrack algorithm is improved, and the Apparence-Spatial Similarity Matrix, (ASM) that integrates the spatial position, operation state and appearance features of the target is introduced, and the trajectory prediction is optimized by the acceleration correction function, which increases the multi-object tracking accuracy (MOTA) by 2.6% and reduces the number of ID switches by 21 times. In addition, a multi-level inspection system is built, integrating data collection, real-time detection and multi-machine collaborative scheduling functions, and relying on 5G and ad hoc network technology to achieve data transmission and remote monitoring. The experimental results show that the proposed scheme significantly improves the accuracy and stability of equipment detection and tracking in the open-pit mine scene, and provides a practical technical solution for intelligent and unmanned inspection of mines. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 针对露天矿传统人工巡检效率低、故障设备与非合作目标识别难、安全风险高及小目标检测 精度不足等问题, 同时为补充 UWB 室内外定位、车载捷联惯性导航等主动定位技术在信号盲区、 非合作目标监测等场景的局限, 提出轻量化目标检测与多目标跟踪算法, 并构建智能化无人机巡检 系统。在检测模型设计上, 主干网络引入可变形卷积 DCNv2, 颈部采用渐进特征金字塔网络 AFPN, 强化多尺度特征提取能力; 设计轻量化检测头 LSDECD-Head, 结合 Focaler-GIoU 损失函数, 提升小 目标与遮挡目标检测精度; 通过 LAMP 剪枝算法实现模型压缩, 30% 剪枝率下仍保持 mAP50 为 0.868、推理时间 196 ms 的性能, 适配无人机计算资源约束。在多目标跟踪方面, 改进 ByteTrack 算 法, 引入融合目标空间位置、作业状态及外观特征的空间−外观相似度矩阵, 结合加速度校正函数优 化轨迹预测, 使多目标跟踪准确率提升 2.6%, ID 切换次数减少 21 次。此外, 构建多层级巡检系统, 集成数据采集、实时检测与多机协同调度功能, 依托 5G 与自组网技术实现数据传输与远程监控。试 验结果表明, 该方案在露天矿场景中显著提升设备检测与跟踪的精度及稳定性, 为矿山智能化、无 人化巡检提供可落地的技术方案。 [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Data: 露天矿无人机巡检: 轻量化目标检测与 多目标跟踪技术及系统构建.
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  Data: UAV inspection in open-pit mine: lightweight target detection and multi-target tracking technology and system construction.
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  Data: <searchLink fieldCode="AR" term="%22刘光伟%22">刘光伟</searchLink><relatesTo>1</relatesTo><i> liuguangwei@lntu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22雷+健%22">雷 健</searchLink><relatesTo>1</relatesTo><i> jianl2025@163.com</i><br /><searchLink fieldCode="AR" term="%22张+磊%22">张 磊</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22罗+霄%22">罗 霄</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22牛喜洋%22">牛喜洋</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22张浩博%22">张浩博</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22袁+杰%22">袁 杰</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22秦飞龙%22">秦飞龙</searchLink><relatesTo>5,6</relatesTo><br /><searchLink fieldCode="AR" term="%22谯乾林%22">谯乾林</searchLink><relatesTo>7</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Coal+Science+%26+Technology+%280253-2336%29%22">Coal Science & Technology (0253-2336)</searchLink>. Feb2026, Vol. 54 Issue 2, p462-481. 20p.
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  Data: *<searchLink fieldCode="DE" term="%22Multiple+target+tracking%22">Multiple target tracking</searchLink><br />*<searchLink fieldCode="DE" term="%22Drone+surveillance%22">Drone surveillance</searchLink><br />*<searchLink fieldCode="DE" term="%22Mines+%26+mineral+resources%22">Mines & mineral resources</searchLink><br />*<searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br />*<searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: In order to supplement the limitations of active positioning technologies such as UWB indoor and outdoor positioning and vehicle-mounted strap-on inertial navigation in signal blind spots and non-cooperative target monitoring, this paper proposes lightweight target detection and multi-object tracking algorithms, and constructs an intelligent UAV inspection system. In the design of the detection model, the deformable convolutional DCNv2 and AFPN are introduced in the backbone network, and the progressive feature pyramid network AFPN is used to strengthen the multi-scale feature extraction ability. The lightweight detection head LSDECD-Head is designed to improve the detection accuracy of small and occluded targets combined with the Focaler-GIoU loss function. The model compression is realized by the LAMP pruning algorithm, and the performance of mAP50 of 0.868 and inference time of 196 ms is still maintained at 30% pruning rate, which adapts to the constraints of UAV computing resources. In terms of multi-object tracking, the ByteTrack algorithm is improved, and the Apparence-Spatial Similarity Matrix, (ASM) that integrates the spatial position, operation state and appearance features of the target is introduced, and the trajectory prediction is optimized by the acceleration correction function, which increases the multi-object tracking accuracy (MOTA) by 2.6% and reduces the number of ID switches by 21 times. In addition, a multi-level inspection system is built, integrating data collection, real-time detection and multi-machine collaborative scheduling functions, and relying on 5G and ad hoc network technology to achieve data transmission and remote monitoring. The experimental results show that the proposed scheme significantly improves the accuracy and stability of equipment detection and tracking in the open-pit mine scene, and provides a practical technical solution for intelligent and unmanned inspection of mines. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 针对露天矿传统人工巡检效率低、故障设备与非合作目标识别难、安全风险高及小目标检测 精度不足等问题, 同时为补充 UWB 室内外定位、车载捷联惯性导航等主动定位技术在信号盲区、 非合作目标监测等场景的局限, 提出轻量化目标检测与多目标跟踪算法, 并构建智能化无人机巡检 系统。在检测模型设计上, 主干网络引入可变形卷积 DCNv2, 颈部采用渐进特征金字塔网络 AFPN, 强化多尺度特征提取能力; 设计轻量化检测头 LSDECD-Head, 结合 Focaler-GIoU 损失函数, 提升小 目标与遮挡目标检测精度; 通过 LAMP 剪枝算法实现模型压缩, 30% 剪枝率下仍保持 mAP50 为 0.868、推理时间 196 ms 的性能, 适配无人机计算资源约束。在多目标跟踪方面, 改进 ByteTrack 算 法, 引入融合目标空间位置、作业状态及外观特征的空间−外观相似度矩阵, 结合加速度校正函数优 化轨迹预测, 使多目标跟踪准确率提升 2.6%, ID 切换次数减少 21 次。此外, 构建多层级巡检系统, 集成数据采集、实时检测与多机协同调度功能, 依托 5G 与自组网技术实现数据传输与远程监控。试 验结果表明, 该方案在露天矿场景中显著提升设备检测与跟踪的精度及稳定性, 为矿山智能化、无 人化巡检提供可落地的技术方案。 [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=191788657
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        Value: 10.12438/cst.2025-1063
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      – Code: chi
        Text: Chinese
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        PageCount: 20
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      – SubjectFull: Multiple target tracking
        Type: general
      – SubjectFull: Drone surveillance
        Type: general
      – SubjectFull: Mines & mineral resources
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      – SubjectFull: Signal detection
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      – SubjectFull: Convolutional neural networks
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      – TitleFull: 露天矿无人机巡检: 轻量化目标检测与 多目标跟踪技术及系统构建.
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              Text: Feb2026
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