XU Y P, CHEN H T, WANG M L, et al.Detection algorithm for underground mine drilling rig withdrawal based on improved YOLOv8n[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(3):59-68.

doi:10.16186/j.cnki.1673-9787.2024090049

Received:2025/02/25

Revised:2025/05/03

Published:2026/04/28

Detection algorithm for underground mine drilling rig withdrawal based on improved YOLOv8n

Xu Yanping1, Chen Huatian1, Wang Manli1, Zhang Shanwen2, Guo Ni3

1.School of Physics & Electronic Information Engineering， Henan Polytechnic University， Jiaozuo  454003， Henan， China；2.School of Electronic Information， Xijing University， Xi’an  710000， Shaanxi， China；3.School of Applied Science and Technology， Beijing Union University， Beijing  100000， China

Abstract: Objectives To eliminate explosion hazards and achieve accurate counting of drill pipes， an improved YOLOv8_SDI drilling rig withdrawal detection algorithm was proposed.  Methods First，to address the issues of a complex imaging environment and limited feature information in the images，the channel attention module SEAttention was introduced. This module employs a self-learning method to assign weight values based on the importance of feature channels，thereby enhancing the algorithm's ability to focus on critical information. Features of information-rich channels were enhanced，while features of irrelevant information channels were suppressed， effectively overcoming the poor extraction of edge features by YOLOv8n. Second，the C2f module in the network was replaced by C2f_DBB，which increases the spatial deformation adaptability of the network， enriches the diversity of the feature space，and further improves the feature expression of the model. The generalization performance of the model is enhanced through the transformation of multi-scale branches. Finally，to address the issues of large gradient loss and slow convergence associated with low-quality images，the Inner_IoU loss function was introduced. The auxiliary anchor box is dynamically adjusted，and the gradient is modified according to the evaluated regression state to overcome the deficiencies of the original CIoU loss function in gradient transfer，thereby improving the prediction accuracy of the anchor box and accelerating the network convergence.  Results The two algorithms were verified on the same drilling withdrawal dataset. The YOLOv8n model  achieved a mean average precision of 97.1%， an accuracy of 95.2%， and a recall of 94.5%. In contrast，the YOLOv8n_SDI model achieved a mean average precision of 98%， an accuracy of 96.4%， and a recall of 96.1%.  Conclusions Compared with the YOLOv8n algorithm， the improved YOLOv8n_SDI algorithm demonstrates better performance in drilling  withdrawal detection， showcasing its value in engineering applications.

Key words:deep learning;object detection;attention mechanism;drill pipe withdrawal;drill rod identification