Design and Research of an Intelligent Detection Method for Coal Mine Fire Edges

Mine fire is caused by external heat source or coal seam spontaneous combustion, and there are serious hidden dangers in mining operation. The existing detection methods have high cost, limited coverage and delayed response. An edge intelligent fire detection system based on multi-source information...

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Veröffentlicht in:Applied sciences Jg. 15; H. 19; S. 10589
Hauptverfasser: Yang, Yingbing, Zhao, Duan, Ge, Yicheng, Li, Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 30.09.2025
Schlagworte:
Accuracy
adaptive attention mechanism
Algorithms
Coal industry
Combustion
Computer vision
Data transmission
Decision making
Deep learning
Edge computing
edge of computing
Equipment and supplies
fire external cause detection
Fire extinction
Fuzzy logic
Fuzzy sets
Human performance
Image retrieval
Internet of Things
Methods
Mine accidents
Mineral industry
Mining
Mining industry
multi-source information fusion
Sensors
Surveillance
Telecommunication systems
Time series
YOLOv5s object detection algorithm
ISSN:2076-3417, 2076-3417
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Zusammenfassung:Mine fire is caused by external heat source or coal seam spontaneous combustion, and there are serious hidden dangers in mining operation. The existing detection methods have high cost, limited coverage and delayed response. An edge intelligent fire detection system based on multi-source information fusion is proposed. We enhance the YOLOv5s backbone network by (1) optimized small-target detection and (2) adaptive attention mechanism to improve recognition accuracy. In order to overcome the limitation of video only, a dynamic weighting algorithm combining video and multi-sensor data is proposed, which adjusts the strategy according to the real-time fire risk index. Deploying quantitative models on edge devices can improve underground intelligence and response speed. The experimental results show that the improved YOLOv5s is 7.2% higher than the baseline, the detection accuracy of the edge system in the simulated environment is 8.28% higher, and the detection speed is 26% higher than that of cloud computing.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app151910589