Traffic Target Detection And Recognition Based On Edge Computing

Transportation is an important part of social production and human life.In recent years,with the increase in the number of motor vehicles,the safety problems,environmental damage and economic losses caused by transportation have become increasingly serious.People have begun to use Intelligent Traffic System(ITS)to solve problems.Traffic target detection is a basic function of intelligent transportation.A real-time object detection platform with high detection accuracy is of great significance for the development of intelligent transportation systems.Because of the low detection accuracy,the traditional object detection method has been replaced by the object detection method based on deep learning.However,the high computational complexity of deep learning greatly limits the detection speed.Edge Computing is aims to transfer computing tasks and decision centers from the cloud to the edge of the network to achieve rapid r esponse to delay-sensitive application scenarios.In this paper,a traffic target detection architecture based on edge computing is proposed,which is combined with the deep learning detection algorithm Mask R-CNN for object detection.This paper first introduces the theoretical knowledge of deep learning and object detection,and then designs the respective tasks of the three modules of the terminal,edge and cloud in the proposed architecture.The terminal is responsible for collecting and compressing dat a,the edge server is responsible for most of the detection tasks,and the cloud server is responsible for a part of difficult object detection tasks and the training of all object detection models.The rules of the task allocation between the cloud and th e edge and the training strategy of the object detection model of edge server are hyper-parametrically tuned to achieve the best balance between efficiency and accuracy.For the object detection model embedded in the edge server,this paper uses the lightw eight neural network Mobile Net-V3 as the backbone network of the model to better adapt to the hardware environment of the edge server,and uses the standard neural network Res Net-50 as the backbone network of the model in the cloud server to achieve the be st detection performance.Finally,the proposed architecture is simulated and compared with the traffic target detection architecture based on cloud computing.The simulation results show that the detection speed of traffic target detection based on edge computing can be improved by more than 2 times at the expense of only a little detection accuracy(about 5%m AP),and the consumption of network bandwidth is only 1 / 3 of the latter.