Cited by YOLO-PR: Multi Pose Object Detection Method for Underground Coal Mine

Straightness measurement of conveyors based on SINS/UWB with a Robust Laplace Kalman filter DOI

Yuming Chen, Lijiang Wei,

YuXin Du

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116978 - 116978

Published: Feb. 1, 2025

Language: Английский

Citations

Self-Supervised Crf Transformers for Tunnel Face Extraction in Complex Environments DOI

Xiaoting Zhao, Yulin Ding,

Rui Hao

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

A small object detection algorithm for mine environment DOI

Dong Liu, Xin Zhao, Weiqiang Fan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 153, P. 110936 - 110936

Published: May 2, 2025

Language: Английский

Citations

An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines DOI

Pengfei Zhao,

Xichao Wang, Weijian Yu

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: May 13, 2025

The underground drilling environment in coal mines is critical and prone to accidents, with common accident types including rib spalling, roof falling, others. High-quality datasets are essential for developing validating artificial intelligence (AI) algorithms mine safety monitoring automation field. Currently, there no comprehensive benchmark dataset industrial scenarios, limiting the research progress of AI this industry. For first time, study constructed a (DsDPM 66) specifically operations, containing 105,096 images obtained from surveillance videos multiple operation scenes. has been manually annotated support computer vision tasks such as object detection pose estimation. In addition, conducted extensive benchmarking experiments on dataset, applying various advanced but not limited YOLOv8 DETR. results indicate proposed highlights areas improvement algorithmic models fills data gap mining, providing valuable resources monitoring.

Language: Английский

Citations

Slim-YOLO-PR_KD: an efficient pose-varied object detection method for underground coal mine DOI

Huaxing Mu,

Jueting Liu,

Yanyun Guan

et al.

Journal of Real-Time Image Processing, Journal Year: 2024, Volume and Issue: 21(5)

Published: Aug. 28, 2024

Language: Английский

Citations

AM-CFDN: semi-supervised anomaly measure-based coal flow foreign object detection network DOI

Weidong Li, Yongbo Yu, Chisheng Wang

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 30, 2024

Language: Английский

Citations

Robotics in underground coal mining: Enhancing efficiency and safety through technological innovation DOI

Parankush Koul

Podzemni radovi, Journal Year: 2024, Volume and Issue: 45, P. 1 - 26

Published: Jan. 1, 2024

The aim of this paper is to explore how robotics can be applied underground coal mining in order make operations more efficient and safer with the help technology. It calls for use regulations developed by industry bodies including National Institute Occupational Safety Health (NIOSH) Mine Administration (MSHA) ensure that are used safely efficiently mining. study also points NIOSH efforts resolve health safety issues around automation technology industry. When high-tech robotic equipment deployed, it demonstrates great productivity gains less human suffering from disease. In demonstrating these innovations, proposes must continually innovated maximize extraction resources worker safety, positioning robots as new force

Language: Английский

Citations

YOLO-PR: Multi Pose Object Detection Method for Underground Coal Mine DOI

Wei Chen,

Huaxing Mu,

Dufeng Chen

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 157 - 167

Published: Jan. 1, 2024

Language: Английский

Citations