School of Electrical and Information Engineering, Anhui University of Science and Technology
Institute of Environment-friendly Meterials and Occupational Health, Anhui University of Science and Technology

为提高煤矿井下钻杆计数的效率和精度，提出了一种基于改进YOLOv8n模型的煤矿井下钻杆计数方法。建立了YOLOv8n−TBiD模型，该模型可准确检测矿井钻机工作视频中的钻杆并进行有效分割：为有效捕获钻杆的边界信息，提高模型对钻杆形状识别的精度，使用加权双向特征金字塔网络（BiFPN）替换路径聚合网络（PANet）；针对钻杆易与昏暗的矿井环境混淆的问题，在Backbone网络的SPPF模块后添加三分支注意力（Triplet Attention），以增强模型抑制背景干扰的能力；针对钻杆在图像中占比小、背景信息繁杂的问题，采用Dice损失函数替换 CIoU损失函数来优化模型对目标钻杆的分割处理。利用YOLOv8n−TBiD模型分割出的钻杆及其掩码信息，根据打钻过程中钻杆掩码面积变小而装新钻杆时钻杆掩码面积突然增大的规律，设计了一种钻杆计数算法。选取综采工作面实际采集的钻机工作视频对基于YOLOv8n−TBiD模型的钻杆计数方法进行了实验验证，结果表明：① YOLOv8n−TBiD模型检测钻杆的平均精度均值达94.9%，与对比模型GCI−YOLOv4，ECO−HC，P−MobileNetV2，YOLOv5，YOLOX相比，检测准确率分别提升了4.3%，7.5%，2.1%，6.3%，5.8%，检测速度较原始YOLOv8n模型提升了17.8%。② 所提钻杆计数算法在不同煤矿井下环境的视频数据集上实现了99.3%的钻杆计数精度。

In order to improve the efficiency and precision of underground drill pipe counting in coal mines, a coal mine underground drill pipe counting method based on the improved YOLOv8n model is proposed. The YOLOv8n-TbiD is established.The model can accurately detects and segments drill pipes in mine drilling rig working videos. The main improvements include the following points. In order to effectively capture the boundary information of drill rods and improve the precision of the model in recognizing drill rod shapes, the weighted bidirectional feature pyramid network (BiFPN) is used instead of the path aggregation network (PANet). To address the issue of drill pipe objects being easily confused with dim mine environments, Triplet Attention is added to the SPPF module of the Backbone network to enhance the model's capability to suppress background interference. In response to the small proportion of drill pipes in the image and the complexity of background information, the Dice loss function is used to replace CIoU loss function to optimize the segmentation processing of drill pipe objects in the model. The method uses the YOLOv8n-TBiD model to segment the drill pipe and its mask information. A drill pipe counting algorithm is designed based on the rule that the mask area of the drill pipe decreases during drilling and suddenly increases when a new drill pipe is installed. The working video of the drilling rig in the fully mechanized working face is selected, in order to conduct experimental verification of drill pipes counting method based on YOLOv8n-TBiD model. The experimental results show that the mean average precision of the YOLOv8n-TBiD model for detecting drill pipes reaches 94.9%. Compared with the comparative experimental models GCI-YOLOv4, ECO-HC, P-MobileNetV2, YOLOv5, and YOLOX, the accuracy increases by 4.3%, 7.5%, 2.1%, 6.3%, and 5.8%, respectively, and the detection speed increases by 17.8% compared to the original YOLOv8n model. The proposed drill pipe counting algorithm achieves precision of 99.3% on video datasets from different underground coal mine environments.

安徽省重点研究与开发计划项目（202104g01020012）；安徽理工大学环境友好材料与职业健康研究院研发专项基金资助项目（ALW2020YF18）。

姜媛媛，刘宋波. 基于改进YOLOv8n的煤矿井下钻杆计数方法[J]. 工矿自动化，2024，50（8）：112-119.

JIANG Yuanyuan, LIU Songbo. A coal mine underground drill pipes counting method based on improved YOLOv8n[J]. Journal of Mine Automation，2024，50（8）：112-119.