ZHAO W, ZHANG W K, LIU D C, et al. Research on intelligent counting of gas extraction drill rods based on YOLOv5[J]. Journal of Henan Polytechnic University(Natural Science) , 2025, 44(3): 81-88.

doi:10.16186/j.cnki.1673-9787.2024060009

Received:2024/06/06

Revised:2014/12/30

Online:2025-04-18

Research on intelligent counting of gas extraction drill rods based on YOLOv5

ZHAO Wei1, ZHANG Wenkang1, LIU Decheng1, WANG Tao1, WANG Dengke2, XIA Dailin3, ZHOU Lizan3, LI Zhifei3

1.Henan Longyu Energy Co.， Ltd.， Yongcheng  476600， Henan， China；2.State Key Laboratory Cultivation Base for Gas Geology and Gas Control， Henan Polytechnic University， Jiaozuo  454000， Henan， China；3.Wuhan Tensense Geotech Co.， Ltd.， Wuhan  430070， Hubei， China

Abstract: Objectives With the increasing depth of coal mining operations， the safety risks associated with gas extraction have become more prominent. Accurate counting of drill rods is essential for ensuring the safety and efficiency of gas extraction. Traditional methods are often inefficient， error-prone， and struggle to perform reliably in complex underground environments.  Methods This study proposes an intelligent drill rod counting method based on the YOLOv5 deep learning model， enhanced by spatiotemporal information fusion. The method processes underground video data in real time to achieve automatic detection and counting of drill rods. The dataset consists of 28 simulated drill withdrawal scenarios across 7 groups and 10 real-world withdrawal scenarios. To improve model robustness， data augmentation techniques such as overexposure， underexposure， smoke interference， and mirroring were employed. Four YOLOv5 variants （YOLOv5s， YOLOv5m， YOLOv5l， YOLOv5x） were compared to identify the most suitable model. Additionally， drill rod count updates were optimized by incorporating features such as abrupt changes in area and Intersection over Union （IoU） values.  Results Experimental results demonstrated that all four YOLOv5 models achieved an accuracy of 99.5% and a recall rate of 100% on the dataset. YOLOv5s was selected for subsequent use due to its balance of accuracy and computational efficiency.  Conclusions The proposed method achieved 100% correct counting in both simulated and real drill withdrawal scenarios， demonstrating excellent accuracy and robustness. By minimizing manual intervention， it significantly enhances the automation of drill rod counting and shows strong potential for application in coal mine safety and other industrial automation monitoring fields.

Key words: drill rod counting; object detection; spatiotemporal fusion; deep learning; gas extraction