GUO L Z, ZHU P F,et al.Recognition of hydraulic support push-pull state in fully mechanized longwall mining face based on machine vision[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):116-124.

doi:10.16186/j.cnki.1673-9787.2024120047

Received:2024/12/19

Revised:2025/09/08

Published:2026-07-24

Recognition of hydraulic support push-pull state in fully mechanized longwall mining face based on machine vision

Guo Longzhen1,2, Zhu Pengfei3

1.School of Electrical Engineering and Automation， Henan Polytechnic University， Jiaozuo  454003， Henan， China；2.Zhengzhou Coal Mining Machinery Group Co.， Ltd.， Postdoctoral Research Station， Zhengzhou  450000， Henan， China；3.Zhengzhou ZMJ Intelligent Control Technology Innovation Center Co.， Ltd.， Zhengzhou  450000， Henan， China

Abstract: Objectives Addressing the issues of low efficiency and poor reliability caused by the heavy reliance on manual inspection and sensor monitoring for the push-and-pull states of hydraulic supports in fully mechanized coal mining faces， this paper proposes a support state recognition method based on machine vision to achieve real-time perception and intelligent operation and maintenance of the working face. Methods The proposed method adopts a top-down framework for keypoint-based analysis of hydraulic support bases. In the target detection stage， the YOLOv8m network is employed to extract visual features and obtain the positions of support bases in the image coordinate system. Based on the detected results， the spatial distribution of supports is analyzed， and a Spatial Confidence Enhancement Matrix （SCEM） is constructed to improve localization reliability. The SCEM is integrated into the YOLOv8m detection head for refined post-processing to enhance detection accuracy. In the keypoint detection stage， a dedicated dataset of hydraulic support base keypoints is constructed， and the network architecture of Lite-HRNet is modified to train a keypoint detection model. Finally， the motion state of each support is determined by analyzing temporal variations of keypoint positions in video sequences.  Results Experimental results demonstrate that the proposed method can accurately recognize the push-pull states of hydraulic supports in fully mechanized mining faces， achieving a recognition accuracy of 88.29%. Conclusions The proposed method shows strong practical applicability and provides effective technical support for underground coal mining operations， promoting the intelligent transformation and upgrading of the coal mining industry.

Key words:intelligent recognition;spatial confidence enhancement matrix;keypoint detection;hydraulic support;push-pull state