【摘要】 罐道木一般用于矿井提升装置停止后的固定，如何检测罐道木的磨损情况对于确保提升装置的稳定运行以及保证地下矿山的正常生产具有重要意义。YOLOv7算法在目标检测任务中表现出色，适用于矿山特殊的工作环境提出的罐道木磨损检测算法模型，通过相机连续采集罐道木磨损变化的图片，再基于YOLOv7算法进行训练，获得训练模型后，检测识别罐道木磨损图片并对算法模型的精度作出评价。模型训练后，罐道木数据集的Loss值最终下降到0.804，反映数据集检测效果的mAP值达到0.938。结果表明，该罐道木磨损检测算法能很好地克服井下恶劣环境并检测罐道木磨损情况，有效提高巡检人员的工作效率，从而确保矿山提升装置的安全。更多还原

【Abstract】 Cannel logs are generally used to fix the mine hoisting device after it stops, how to detect the wear of the cannel logs is of great significance to ensure the stable operation of the hoisting device as well as to ensure the normal production of underground mines.The YOLOv7 algorithm performs well in the target detection task, which is suitable for the special working environment of mines. In this paper, the proposed cannel wood wear detection algorithm model through the camera continuous acquisition of cannel wood wear change pictures, and then based on the YOLOv7 algorithm for training, after obtaining the training model, detection and identification of cannel wood wear pictures and make an evaluation of the accuracy of the algorithm model. The Loss value of the cannel wood dataset after the model training is finally reduced to 0.804, and the mAP value reflecting the detection effect of the dataset reaches 0.938. The results show that the cannel wood wear detection algorithm can overcome the harsh environment of the underground to detect the wear of the cannel wood and can effectively improve the efficiency of the inspectors and ensure the safety of the hoisting device in the mine.更多还原