Deep Learning Traffic Sign Recognition in Autonomous Vehicle

被引：0

作者：

Alhabshee, Sharifah Maryam ^{[1
]}

bin Shamsudin, Abu Ubaidah ^{[2
]}

机构：

[1] Univ Tun Hussein Onn Malaysia, Fac Elect & Elect Engn, Johor Baharu, Malaysia

[2] Univ Tun Hussein Onn Malaysia, Fac Elect & Elect Engn, MINT SRC, Johor Baharu, Malaysia

来源：

2020 18TH IEEE STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT (SCORED) | 2020年

基金：

奥地利科学基金会;

关键词：

Deep learning; YOLOv3; Recognition; Traffic sign;

D O I：

10.1109/scored50371.2020.9251034

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, a deep learning method is used to make a system for traffic sign recognition. You Only Look Once (YOLOv3) is used as it has a quick response in terms of real-time data reliability followed by high accuracy and robust performance. This study applies image preprocessing for better decision making for the recognition system in a different environment which includes lighting and weather. This is to ensure that the approach used is safe to be installed in autonomous vehicles. A comparison of images trained and tested will be demonstrated. The accuracy reach up to 100% and time to recognize traffic sign in image is in 36.907457 seconds. An analysis is done to ensure the error rate is reduced as training is done in a longer period.

引用

页码：438 / 442

页数：5

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