Research And Implementation Of Pedestrian Detection Algorithm For Mining Inspection Robot Based On Jetson TX2

Mine inspection robot is an important auxiliary equipment in the field of safe production of coal mines,which is of great significance for improving the efficiency of inspection and ensuring the safety of underground production.At present,mining inspection robots are limited to the collection of surrounding environmental parameters and equipment status information,and do not have the function of real-time inspection of underground workers.At the same time,due to the adverse conditions of the underground environment,insufficient lighting,mixed image background,and poor image acquisition quality,traditional pedestrian detection methods cannot be directly applied to mining inspection robots,and it is difficult to achieve real-time detection and alarm of the dangerous behaviors(such as near the head,tail,tensioning device and other easy-to-clamp parts of the belt conveyor,across the conveyor belt,etc)for workers along the inspection line,as well as staff on-the-spot inspection functions.The purpose of this thesis is to research and propose a deep learning-based mine pedestrian detection algorithm,which is deployed in the embedded platform Jetson TX2,to realize the automatic detection of underground workers of mining inspection robots.Firstly,an overall design scheme of mining inspection robot based on Jetson TX2 was proposed,and the design method of pedestrian detection module was analyzed in detail.Then,based on the improved SSD(Single Shot Multi Box Detector)algorithm,a mine pedestrian detection algorithm was proposed to optimize the aspect ratio and number of the default boxes in each prediction layer of the SSD network model.We designed a lightweight feature extraction network and a prediction module based on residuals.Experimental results show that the algorithm has an average accuracy rate of 88.9% in the mine pedestrian test set and a detection speed of 14 fps in the embedded platform Jetson TX2.In order to further improve the detection efficiency of the algorithm in the embedded platform Jetson TX2,the algorithm was compressed and optimized using Tensor RT inference acceleration technology.The experimental results show that the average accuracy of the algorithm after acceleration and optimization is 87.2%,the detection speed is 48 fps.Then the accelerated algorithm was tested in the laboratory environment.The results show that the algorithm can effectively detect pedestrian targets in the video area and give corresponding alarm prompts to achieve real-time detection of pedestrians in the video.But pedestrians with longer distances and smaller scales may slightly miss detection.The mine pedestrian detection algorithm based on improved SSD proposed in this thesis can be used in mine inspection robots to automatically detect underground workers,and it can also be used in underground fixed point monitoring and auxiliary driving systems for underground locomotives,which has high application value.