Detection of coal fire by deep learning using ground penetrating radar

Coal fire seriously endangers coal resources. Accurate detection of its combustion range is the basis of disaster control. In this paper, a deep learning-based method of recognizing coal fire using ground-penetrating radar (GPR) is proposed, which improves the accuracy and speed of delineating coal fire areas. The self-built coal fire physical model is scanned by the GPR, and the radar images are obtained. The test results are compared with GPR images to summarize the spatial evolution law of coal fire areas and interpret the signal characteristics of the coal fire in radar images. The signal characteristics include combustion cavity, combustion surface, and underground combustion collapse surface. Comparing different algorithms, the results show that the YOLOv5l has the highest detection accuracy, which meets the need for detection of the coal fire. The proposed method lays the foundation for the detection of the combustion range in the coal fire areas.

Automated summary

This paper proposes a deep learning-based method using ground-penetrating radar (GPR) for recognizing coal fire, which improves the accuracy and speed of delineating coal fire areas. By scanning a self-built coal fire physical model with GPR and comparing the results with GPR images, the spatial evolution law of coal fire areas and the signal characteristics of coal fire in radar images, including combustion cavity, combustion surface, and underground combustion collapse surface, are summarized. The results show that YOLOv5l achieves the highest detection accuracy among different algorithms, meeting the need for coal fire detection. This method lays the foundation for detecting the combustion range in coal fire areas.