基于退钻过程检测的矿井下钻杆计数方法研究

Research on drill rod counting method in mines based on drill withdrawal process detection

  • 摘要: 针对现有的矿井下钻杆计数方法存在误检率较高、效率较低的问题,设计一种特征编码式计数方法,其主要通过对钻机退钻过程检测结果的分析处理,统计实际打入的钻杆数量,可以有效提高计数的准确性及工作效率。由于矿井下的环境十分复杂,由视频监控获取到的图像易受噪声、灯光等因素的影响,造成现有的目标检测算法存在特征提取困难和识别率低等问题。并且,这些算法模型复杂度高且计算量大,不利于在边缘端进行部署。针对这些问题,提出一种基于 YOLOv8n 改进的钻机退钻过程检测算法,称为YOLOv8n–SDM。首先,设计新的空间金字塔池化模块来增强模型的特征提取能力,同时降低矿井下复杂背景对退钻检测的干扰;然后,提出具有2种变换结构的特征聚合模块,替换原模型中的C2f模块,其在获取丰富的特征信息的同时有效降低模型的复杂度与计算量;最后,为进一步提升模型对不规则特征的辨识能力,设计了一种多尺度融合探测头来提高模型对钻机等目标的识别率。实验结果表明:改进后的YOLOv8n–SDM算法和原YOLOv8n算法相比,精度、召回率、mAP@0.5以及mAP@0.5–0.95值分别提升了2.7%、2.6%、2.2%以及1%,模型参数量、占用存储空间和GFLOPs值分别降低了32.2%、30.2%和31.7%。相较主流算法能够在复杂的矿井场景下实现更精准的钻机退钻过程检测,满足实际部署和应用的需求。

     

    Abstract: In view of the problems of high false detection rate and low efficiency of existing drill rod counting methods in mines, a feature encoding counting method is designed, which mainly counts the number of drill rods actually driven in by processing and analyzing the results of the drill rig withdrawal process detection, which can effectively improve the counting accuracy and work efficiency. Due to the complex environment in the mine, the images obtained by video surveillance are easily affected by factors such as noise and lighting, resulting in the existing target detection algorithms having problems such as difficulty in feature extraction and low recognition rate. In addition, these algorithm models are highly complex and computationally intensive, which is not conducive to deployment on the edge. To address these problems, an improved YOLOv8n drill rig withdrawal process detection algorithm, called YOLOv8n–SDM, is proposed. Firstly, a new spatial pyramid pooling module is designed to enhance the feature extraction ability of the model and reduce the interference of the complex background in the mine on the drill withdrawal detection. Then, a feature aggregation module with two transformation structures is proposed to replace the C2f module in the original model, which can effectively reduce the complexity and calculation of the model while obtaining rich feature information. Finally, in order to further improve the model's ability to recognize irregular features, a multi-scale fusion detector is designed to improve the model's recognition rate of targets such as drilling rigs. Experimental results show that compared with the original YOLOv8n algorithm, the precision, recall rate, mAP@0.5 and mAP@0.5–0.95 values of the improved YOLOv8n–SDM algorithm are increased by 2.7%, 2.6%, 2.2% and 1% respectively, and the model parameters, storage space occupied and GFLOPs values are reduced by 32.2%, 30.2% and 31.7% respectively. Compared with the mainstream algorithm, it can achieve more accurate detection of the drill withdrawal process in complex mine scenes, meeting the needs of actual deployment and application.

     

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