基于深度学习的矿井巷道人员计数技术

Mine roadway personnel counting technology based on deep learning

在煤矿发生安全事故时需要明确掌握各个区域的人员情况,合理安排营救计划。使用YOLOv5作为目标检测器,结合改进的DeepSORT跟踪算法进行矿井人员跟踪,实现煤矿矿井各个巷道区域的人员计数;首先使用轻量化的全尺度特征学习Re-ID特征提取模型OSNet对DeepSORT进行优化,替换原有的CNN特征提取模块;然后采用检测器和OSNet特征提取模型单独训练的策略,实现了矿井复杂环境下稳定的跟踪效果;在此基础上,通过在视频画面中设置ROI区域和基准线来判断人员进出的情况,从而实现计数功能。为了有效训练和评估模型的性能,采集了10 000张煤矿矿井下各个巷道不同区域的图片用于训练和测试,改进后模型的MOTA为66.7%,优于改进前的63.4%;改进后速度为28.1 FPS,优于改进前的25.3 FPS。试验结果表明:改进后的模型可以有效地实现矿井人员计数,可以用到实际的生产环境中。

When a safety accident occurs in a coal mine, it is necessary to clearly understand the personnel situation in each area and reasonably arrange the rescue plan. In this paper, YOLOv5 is used as the target detector, combined with the improved DeepSORT tracking algorithm to track the mine personnel, and the personnel count in each roadway area of the mine is realized.Firstly, the lightweight full-scale feature learning Re-ID feature extraction model OSNet is used to optimize DeepSORT and replace the original CNN feature extraction module. Then, the strategy of training the detector and OSNet feature extraction model separately is adopted to achieve a stable tracking effect in the complex environment of the mine. On this basis, the ROI area and baseline are set in the video screen to judge the situation of people entering and leaving, so as to realize the counting function. In order to effectively train and evaluate the performance of the model, 10 000 pictures of different areas of various roadways under coal mines were collected for training and testing. The MOTA of the improved model was 66.7%, better than that of the former63.4%. The improved speed is 28.1 FPS, which is better than the 25.3 FPS before the improvement. The experimental results show that the improved model can effectively count mine personnel and can be used in the actual production environment.