Mine roadway personnel counting technology based on deep learning

Abstract

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 former 63.4%. 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.

Keywords

• mine personnel tracking
• people counting
• yolov5
• deepsort
• osnet