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Lightweight neural network combined with depth camera for miner target detection and localization
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Miao ZHANG1, 2, Xiaojun WANG1, 2, Jingfa LEI1, 2, 3, **, Ruhai ZHAO1, 2, Yongling LI1, 2
China Safety Science Journal | 2025, 35(3) : 115 - 124
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China Safety Science Journal | 2025, 35(3): 115-124
Safety engineering technology
Lightweight neural network combined with depth camera for miner target detection and localization
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Miao ZHANG1, 2, Xiaojun WANG1, 2, Jingfa LEI1, 2, 3, **, Ruhai ZHAO1, 2, Yongling LI1, 2
Affiliations
  • 1 School of Mechanical and Electrical Engineering,Anhui Jianzhu University,Hefei Anhui 230601,China
  • 2 Key Laboratory of Intelligent Manufacturing of Construction Machinery,Anhui Education Department,Hefei Anhui 230601,China
  • 3 Sichuan Provincial Key Laboratory of Process Equipment and Control Engineering,Zigong Sichuan 643000,China
Published: 2025-03-28 doi: 10.16265/j.cnki.issn1003-3033.2025.03.0863
Outline
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To prevent miners from mistakenly entering dangerous areas,a lightweight underground miner object detection model based on YOLOv5s-MPD was proposed,which combined with depth camera to locate miner targets and detect whether miners had entered dangerous areas in real time. Specifically,the MobileNetv3 lightweight neural network was used as the backbone feature extraction network to significantly reduce the model size. Secondly,Polarized Self-Attention (PSA) module was introduced to enhance the perception of targets. Finally,Deformable Convolution Network v2 (DCNv2) was used to replace the standard convolution in the C3 module of the feature fusion layer,solving the problem of partial feature information loss in conventional convolution. The improved model was used in combination with the color images obtained by the depth camera to detect miner targets and obtain the spatial three-dimensional coordinates of the target center points. The results show that compared with YOLOv5s,the improved model reduces the number of parameters and computation by 83.54% and 77.03%,respectively. The model size is only 3.4 MB,and a detection speed of 70.2 f/s,which is increased by 54.97%. The mean average precision is 0.825. Compared with mainstream object detection models,the improved model has a more balanced number of parameters,computation,model size,detection speed,and mean average precision. In the actual positioning accuracy test,within a range of 1-8 meters,the average absolute error and average relative error of the distance between the camera and the miner target were 0.11 meters and 1.74%,respectively. The maximum absolute error and maximum relative error were 0.25 meters and 2.96%,respectively. In the dynamic detection,the miner target could be detected and its location information output,with a detection success rate of 97.5%.

lightweight  /  neural network  /  deep camera  /  target detection  /  target localization  /  security warning
Miao ZHANG, Xiaojun WANG, Jingfa LEI, Ruhai ZHAO, Yongling LI. Lightweight neural network combined with depth camera for miner target detection and localization[J]. China Safety Science Journal, 2025 , 35 (3) : 115 -124 . DOI: 10.16265/j.cnki.issn1003-3033.2025.03.0863
Year 2025 volume 35 Issue 3
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.03.0863
  • Receive Date:2024-10-23
  • Online Date:2025-07-05
  • Published:2025-03-28
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  • Received:2024-10-23
  • Revised:2024-12-25
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Affiliations
    1 School of Mechanical and Electrical Engineering,Anhui Jianzhu University,Hefei Anhui 230601,China
    2 Key Laboratory of Intelligent Manufacturing of Construction Machinery,Anhui Education Department,Hefei Anhui 230601,China
    3 Sichuan Provincial Key Laboratory of Process Equipment and Control Engineering,Zigong Sichuan 643000,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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