Research on the Detection of Traffic Flow based on Video Images

Jian He, Wei Teng, Zeyu Zhao, Binche Liu, Bing Qin, Jun Jiang
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Abstract

Based on the current level of social development, everyone's demand for cars has increased rapidly. At present, the total number of motor vehicles and drivers in China ranks first in the world. With the rapid development of deep learning, the method of vehicle flow statistics based on video can directly use the existing traffic monitoring camera to realize the detection of vehicles, and some traffic flow detection based on YOLOv1, YOLOv2, YOLOv3, YOLOv4 and other algorithms have problems such as insufficient accuracy and low efficiency. Therefore, this paper proposes to use YOLOv5 to replace the original algorithm to achieve object detection, tracking, and processing. I improve the efficiency of the statistics of the traffic flow.
基于视频图像的交通流检测研究
基于当前的社会发展水平,每个人对汽车的需求都在快速增长。目前,我国机动车保有量和驾驶人总数均居世界第一。随着深度学习的快速发展,基于视频的车辆流量统计方法可以直接利用现有的交通监控摄像头实现对车辆的检测,而一些基于YOLOv1、YOLOv2、YOLOv3、YOLOv4等算法的交通流量检测存在准确率不够、效率低等问题。因此,本文提出用 YOLOv5 代替原有算法,实现物体的检测、跟踪和处理。一、提高流量统计效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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