Evaluation of Real-Time Perception of Deformation State of Host Rocks in Coal Mine Roadways in Dusty Environment

Pengfei Shan, Chengwei Yan (), Xingping Lai, Haoqiang Sun, Chao Li and Xingzhou Chen
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Pengfei Shan: School of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Chengwei Yan: School of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xingping Lai: School of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Haoqiang Sun: School of Energy Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Chao Li: Key Laboratory of Western Mines and Hazard Prevention of Ministry of Education, Xi’an University of Science and Technology, Xi’an 710054, China
Xingzhou Chen: Key Laboratory of Western Mines and Hazard Prevention of Ministry of Education, Xi’an University of Science and Technology, Xi’an 710054, China

Sustainability, 2023, vol. 15, issue 3, 1-23

Abstract: Intelligent mining needs to achieve real-time acquisition of surrounding rock deformation data of roadways and analysis and provide technical support for intelligent mining construction. To solve problems such as significant error, information lag, and low acquisition frequency of surrounding rock monitoring technology at the current stage, a perception method, RSBV of roadway deformation situation, based on binocular vision is proposed, which realizes the dynamic, accurate real-time acquisition of host rocks’ relative deformation in a dusky environment. The low illumination image enhancement method is used to preprocess original images, which reduces the impact of low illumination and high dust; the K-medoids algorithm segments the target image, and the SIFT algorithm extracts feature points from the ROI (region of interest). The influence of eliminating background images on the feature point extraction is revealed, and the efficiency of feature extraction is improved; the method of SIFT feature-matching with epipolar constraints is studied, which improves the accuracy and speed of feature points. The roadway deformation characteristics are analyzed, and the reflective target is used as the monitoring point. A roadway deformation acquisition and analysis platform based on binocular vision is built in a dim environment. Zhang’s method is selected to calibrate the camera parameters, and stereo rectification is carried out for the target motion image. The adaptability of the RSBV method to different surrounding rock deformation scales is studied and compared with the measurement results of the SGBM algorithm. The results show that the error of the RSBV method is controlled within 1.6%, which is 2.88% lower than the average error of the SGBM algorithm. The average time for processing a group of binocular images is 1.87 s, which is only 20% of the SGBM algorithm. The research result provides a reliable theoretical basis for the real-time and accurate evaluation of the surrounding rock deformation mechanism.

Keywords: mining roadway; dusky environment; surrounding rock deformation; binocular stereo vision; image features matching (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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