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Mine Fire Smoke Target Detection Based on Improved YOLOv10s
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Yeqiu WU1, Zelai SHI2, Huimin ZHU2, Yue LIU1
Mining Research and Development | 2025, 45(9) : 200 - 207
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Mining Research and Development | 2025, 45(9): 200-207
Mine Fire Smoke Target Detection Based on Improved YOLOv10s
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Yeqiu WU1, Zelai SHI2, Huimin ZHU2, Yue LIU1
Affiliations
  • 1.College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, Shanxi 037000, China
  • 2.College of Coal Engineering, Shanxi Datong University, Datong, Shanxi 037000, China
Published: 2025-09-25
Outline
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Mine fire smoke target detection is one of the important preventive measures to prevent fire accidents in mines. In order to improve the accuracy and speed of fire smoke target detection in mines, a mine fire smoke target detection scheme based on improved YOLOv10s was proposed. Firstly, the collected mine fire smoke image dataset was divided into a training set, validation set and test set according to 7:2:1 after annotation on the online annotation platform. Before training, the images were subjected to wavelet denoising and Auto-Msrcr algorithm enhancement processing, then the original YOLOv10 loss function was reconstructed using Focaler-IoU. The attention module CBAM that strengthened the performance of convolutional neural networks was added, and the third C2f module in Neck was replaced by the cross-stage partial network module VoV-GSCSP, thereby improving the representation ability and decision-making performance of the model. Finally, the feature weights of mine fire smoke recognition were improved. The experimental results show that the improved YOLOv10s model has a 3.5% higher accuracy than the original model, the average precision mAP@50 and mAP@50−95 are increased by 0.7% and 2.2% respectively, and the detection frame rate is increased by 11.9 frames/s. Compared with other mainstream YOLO models, the improved YOLOv10s model has higher detection accuracy and speed, as well as better comprehensive detection performance, which is suitable for high-precision and high real-time mine fire smoke detection scenarios.

Mine fire  /  Smoke detection  /  Improved YOLOv10s  /  Attention module  /  Feature weight
武晔秋, 石泽来, 朱慧敏, 刘玥. 基于改进YOLOv10s的矿井火灾烟雾目标检测. 矿业研究与开发, 2025 , 45 (9) : 200 -207 .
Yeqiu WU, Zelai SHI, Huimin ZHU, Yue LIU. Mine Fire Smoke Target Detection Based on Improved YOLOv10s[J]. Mining Research and Development, 2025 , 45 (9) : 200 -207 .
Year 2025 volume 45 Issue 9
112
48
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Article Info
  • Receive Date:2024-10-10
  • Online Date:2026-10-28
  • Published:2025-09-25
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  • Received:2024-10-10
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Affiliations
    1.College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, Shanxi 037000, China
    2.College of Coal Engineering, Shanxi Datong University, Datong, Shanxi 037000, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
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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