An Improved Traffic Sign Detection and Recognition Deep Model Based on YOLOv5
Author:
Affiliation:
1. College of Mathematics and Statistics, Hebei University of Economics and Business, Shijiazhuang, China
2. School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, China
Funder
Science and Technology Project of the Hebei Education Department
Hebei University of Economics and Business Foundation
Hebei Provincial Department of Human Resources and Social Security ‘333 Talent Project’
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10139792.pdf?arnumber=10139792
Reference40 articles.
1. Traffic signs detection and recognition under low-illumination conditions;zhao;Chinese Journal of Engineering,2020
2. M3E-Yolo: A New Lightweight Network for Traffic Sign Recognition
3. Research on multi-target recognition method of traffic scene in complex weather;dong;Inf Commun,2020
4. TSR-YOLO: A Chinese Traffic Sign Recognition Algorithm for Intelligent Vehicles in Complex Scenes
5. CBAM: Convolutional block attention module;woo;Proc Eur Conf Comput Vis,2018
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