An edge computing-based embedded traffic information processing approach:application of deep learning in existing traffic systems  

作　　者：PING Haoyu MA Yongjie ZHU Guangya ZHANG Jiaqi 

机构地区：[1]School of Physics and Electronic Engineering,Northwest Normal University,Lanzhou,730070,China

出　　处：《Optoelectronics Letters》2024年第10期623-628,共6页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.62066041)。

摘　　要：To address traffic congestion,this study improves Mobile Netv2-you only look once version 4(YOLOv4)target detection algorithm(Mobile Netv2-YOLOv4-K++F)and introduces an embedded traffic information processing solution based on edge computing.We transition models initially designed for large-scale graphics processing units(GPUs)to edge computing devices,maximizing the strengths of both deep learning and edge computing technologies.This approach integrates embedded devices with the current traffic system,eliminating the need for extensive equipment updates.The solution enables real-time traffic flow monitoring and license plate recognition at the edge,synchronizing instantaneously with the cloud,allowing for intelligent adjustments of traffic signals and accident forewarnings,enhancing road utilization,and facilitating traffic flow optimization.Through on-site testing using the RK3399PRO development board and the Mobile Netv2-YOLOv4-K++F object detection algorithm,the upgrade costs of this approach are less than one-tenth of conventional methods.Under favorable weather conditions,the traffic flow detection accuracy reaches as high as 98%,with license plate recognition exceeding 80%.

关 键 词：COMPUTING APPROACH TRAFFIC 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U495[自动化与计算机技术—控制科学与工程]