Research On Object Detection And Tracking Algorithm Based On Improved YOLOX

With the rapid development of the intelligent transportation industry,the safety and efficiency of traveling has gradually become the focus of social attention.In view of the shortcomings and deficiencies of existing algorithms in the detection and tracking of moving objects such as vehicles and pedestrians in urban road traffic,as well as the disadvantages and deficiencies of existing algorithms,in this paper,an improved YOLOX object detection algorithm.According to the needs of multiple scenes,three improved Byte Track object trackers are trained,and the experimental verification is completed in the relevant dataset.Finally,a multi-scene object tracking system is designed and implemented.The main contents of this paper are as follows:(1)Through the detailed analysis and comparative experiments of algorithms in the field of object detection and object tracking,based on urban road traffic environment,YOLOX object detection algorithm and Byte Track object tracking algorithm are selected in this paper.In view of the problem of insufficient performance of small object detection of YOLOX object detection algorithm,two optimizations have been made.First,three improved attention modules CBAM are introduced into the network structure,a residual block is removed in the backbone network,and all ordinary convolution layers are replaced with deep separable convolution to improve the accuracy and speed of small object detection;Secondly,DIo U Loss and Focal Loss are used to optimize the network training in order to further improve the network accuracy.Through comparative experiments and ablation experiments on Tiny Person dataset,the effectiveness of the improved YOLOX algorithm is verified.(2)Based on the necessity of object tracking dataset expansion and model generalization ability improvement,this paper uses the Air Sim open-source simulation system to create a simulation dataset for two categories of vehicles and pedestrians,which is recursively named UNU’s not unreal,or UNU for short.The UNU dataset is taken from four video sequences of City Environ and Air Sim NH scenes.Each video sequence is valid for 20 seconds.This dataset enriches the diversity of datasets in the field of object tracking,and lays a necessary foundation for future related research.(3)Taking Byte Track-vehicle,Byte Track-person and Byte Track-both trackers as the core,a multi-scene object tracking system is designed and implemented with front-end and back-end interaction based on Python and Py Side2.The object detection and tracking algorithm proposed in this paper has high precision and speed.The related research can not only be applied to the field of intelligent transportation system and intelligent video surveillance,but also provide some theoretical basis and method reference for tasks such as intelligent visual navigation and modern military.