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
收藏
页码：438 / 442
页数：5
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