Traffic Sign Detection and Recognition using Deep Learning

被引:3
作者
Oza, Rudri Mahesh [1 ]
Geisen, Angelina [1 ]
Wang, Taehyung [1 ]
机构
[1] Calif State Univ Northridge, Dept Comp Sci, Northridge, CA 91330 USA
来源
2021 4TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR INDUSTRIES, AI4I | 2021年
基金
美国国家科学基金会;
关键词
machine learning; neural networks; deep learning; image recognition; CNN; traffic sign detection and recognition;
D O I
10.1109/AI4I51902.2021.00012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Advanced Driver Assistance System includes traffic sign identification and recognition. Traffic signs warn drivers of traffic laws, road conditions, and route directions, assisting them in driving more efficiently and safely. Traffic Sign Recognition is a technique for regulating traffic signals, warning drivers, and commanding or prohibiting specific acts. A quick real-time and reliable automated traffic sign detection and recognition system can assist and relieve the driver, improving driving safety and comfort significantly. For autonomous intelligent driving vehicles or driver assistance systems, automatic identification of traffic signals is also important. This paper aims to use Neural Networks to identify traffic sign patterns. Several image processing methods are used to pre-process the images. Then, to understand traffic sign patterns, Neural Networks stages are performed. To find the best network architecture, the system is trained and validated. The results of the experiments show that traffic sign patterns with complex backgrounds can be classified very accurately.
引用
收藏
页码:16 / 20
页数:5
相关论文
共 20 条
[1]  
ABDI L., 2017, P S APPL COMP, P131
[2]  
[Anonymous], P 16 MIN EURO C 10 M
[3]  
Aoyagi Y, 1996, IEEE IND ELEC, P1838, DOI 10.1109/IECON.1996.570749
[4]  
Bahlmann C, 2005, 2005 IEEE INTELLIGENT VEHICLES SYMPOSIUM PROCEEDINGS, P255
[5]  
Bénallal M, 2003, CCECE 2003: CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3, PROCEEDINGS, P1823
[6]  
Carrivick J., 2016, Structure from Motion in the Geosciences
[7]   An automatic road sign recognition system based on a computational model of human recognition processing [J].
Fang, CY ;
Fuh, CS ;
Yen, PS ;
Cherng, S ;
Chen, SW .
COMPUTER VISION AND IMAGE UNDERSTANDING, 2004, 96 (02) :237-268
[8]  
Gu TY, 2019, Arxiv, DOI arXiv:1708.06733
[9]  
Houben S, 2013, IEEE INT C INTELL TR, P7, DOI 10.1109/ITSC.2013.6728595
[10]  
Kang D. S., 1994, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, P88, DOI 10.1109/IAI.1994.336679