XIE P S, XING J J, DUAN S Y, et al. Research on a vision-based hydraulic support pose model for edge computing[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(2):107-119.

doi:10.16186/j.cnki.1673-9787.2025070018

Received:2025/07/17

Revised:2025/11/10

Published  :2026/01/28

Research on a vision-based hydraulic support pose model for edge computing

Xie Panshi1,2,3, Xing Junjun1,2,3, Duan Siyu1,2,3, Li Guoxin1,2,3

1.College of Energy and Mining Engineering， Xi’ an University of Science and Technology， Xi’ an  710054， Shaanxi， China；2.Key Laboratory of Western Mine Exploitation and Hazard Prevention Ministry of Education， Xi’an University of Science and Technology， Xi’an  710054， Shaanxi， China；3.Shaanxi Key Laboratory of Ground Control， Xi'an  710054， Shaanxi， China

Abstract: Objectives Stability control of hydraulic supports is a major technical challenge in steeply inclined coal seam mining. Influenced by variable geological conditions and changes in the mining environment， supports are prone to operating in unstable states.  Methods An improved YOLOv11n-Pose algorithm was employed to train the hydraulic support pose detection model， achieving holistic posture detection. To meet requirements for model lightweighting， the GhostNet network structure and RepConv modules were introduced into the algorithm's backbone. A self-designed C3k2-RVB-EMA attention mechanism module was incorporated to enhance model robustness. To improve overall model accuracy and convergence efficiency， a comparative analysis of prevalent loss functions—namely CIoU， GIoU， DIoU， EIoU， SIoU， and WIoU—was conducted. Based on this comparison， WIoU （Wise-IoU） was selected as the loss function for the model. Based on a binocular vision model and coordinate transformation relationships， data measurement for prop height， canopy pitch angle， and base deflection angle was achieved. Results The results indicate that the lightweight design reduced the model size by 30.26% compared to the original YOLOv11n-Pose algorithm model. The incorporation of the C3k2-RVB-EMA attention mechanism module and the WIoU loss function increased model accuracy by 1.7%. The improved algorithm model outperformed the existing algorithm models in generating support posture curves， keypoint positions， and output confidence values. In the posture measurement experiments， the maximum relative error of the proposed model's measurement results was within 5.52%. When deployed on an edge computing device platform， the proposed model exhibited advantages over common models in terms of parameter count， computational load， size， and inference time.  Conclusions The proposed improved algorithm model demonstrates high accuracy and robustness for holistic posture detection of supports in steeply inclined coal seams. Deploying the model on edge computing devices enables efficient inference， effectively balancing detection accuracy with lightweight deployment requirements. This provides a feasible technical pathway for posture detection of hydraulic supports in steeply inclined coal seams.

Key words: hydraulic supports; posture detection;YOLOv11n-Pose;lightweight model; model deployment