Research On Target Detection And Tracking Method Of Under-Ground Coal Mine Personnel Based On FCOS

Coal is an important energy source for China’s industrial development and has a very large mining scale every year.However,due to low brightness,high dust content and bad working environment,accidents often occur in coal mines.Therefore,the safety guarantee of mine workers is particularly important,and the detection and tracking of underground pedestrians is of great significance to the safety production of coal mines.In this paper,the video images collected by the underground monitoring device are manually annotated,and a small data set of underground coal mine pedestrian detection and tracking is constructed.Based on this,the detection and tracking of underground coal mine pedestrian is studied.The main contents are as follows:(1)An improved FCOS pedestrian detection algorithm is proposed to solve the problems of insufficient detection accuracy,high real-time requirement,poor environmental conditions and complex pedestrian status in underground coal mine.In this model,a lightweight convolutional neural network ShuffleNet V2 is used to replace the backbone network ResNET50 in FCOS detection algorithm,and the feature pyramid structure in the original network is improved into a top-down and bottom-up path aggregation network.At the same time,the detection head of the original FCOS network is replaced by a lightweight detection head composed of two sets of depth-separable convolution.In the course of experimental training,the generalization ability and robustness of the model are improved by enhancing the scale and color data of downhole pedestrian detection data.Experimental results show that the improved FCOS algorithm can achieve a better balance between accuracy and speed.The mAP of the algorithm can reach 51.9%and the FPS can reach 100 frames/s without losing accuracy.(2)Based on the output results of the improved FCOS target detector,Kalman filter is used for prediction and update.The improved Hungarian matching algorithm is used to correlate and match the tracking trajectories and detection results.Specifically,in order to make full use of come detection frames with low confidence output by the detector due to occlusion and other problems,the output results of the detector are divided into high-score detection frame set and low-score detection frame set through threshold setting.Firstly,the high-score detection frame set is associated and matched with the original tracking track,and then the tracking track of the high-score detection frame that has not been matched successfully before is associated and matched with the low-score detection frame set,mining the valuable target information in the low-score detection frame,so as to reduce the missed detection and improve the continuity of tracking track.Experimental results show that the improved tracking algorithm can significantly improve the tracking performance of the tracker.