基于YOLO V5的改装车辆识别技术研究
Research on Modified Vehicle Identification Technology Based on YOLO V5
李杨 1武海燕 1王凡2
作者信息
- 1. 郑州警察学院,河南 郑州
- 2. 武汉铁路公安处,湖北 武汉
- 折叠
摘要
随着我国汽车保有量持续上升,部分用户追求个性化对车辆进行改装,部分货车司机为增加运输量对车辆进行改装等,给道路交通安全带来巨大影响.为解决非法改装车辆识别问题,文章对改装车辆识别技术进行了研究.本研究以YOLO V5 为模型基础,将主干特征提取网络替换为高度灵活且易于实施的MobileNet V2,并将强化特征提取网络中常规卷积操作全部替换为深度可分离卷积,最终得到一个检测效率高、计算需求小、检测速度快的非法改装车辆检测模型.
Abstract
With the continuous rise of car ownership in China,some users pursue personalization to modify their vehicles,and some truck drivers modify their vehicles to increase the transportation volume,etc.,which brings a great impact on road traffic safety,and in order to solve the problem of illegal modified vehicle identification,the article researches the modified vehicle identification technology.This study takes YOLO V5 as the model base,replaces the backbone feature extraction network with highly flexible and easy-to-implement MobileNet V2,and replaces all the conventional convolution operations in the reinforcement feature extraction network with depth separable convolution.Finally,a detection model for illegally modified vehicles with high detection efficiency,small computational requirements and fast detection speed is obtained.
关键词
YOLO/V5/改装车辆识别/车辆检测技术Key words
YOLO V5/identification of modified vehicles/vehicle detection technology引用本文复制引用
出版年
2024
NETL
NSTL
万方数据