Multiple traffic sign detection based on the artificial bee colony method

被引:8
作者
Banharnsakun, Anan [1 ]
机构
[1] Kasetsart Univ, Fac Engn Sriracha, Comp Engn Dept, Computat Intelligence Res Lab CIRLab, Sriracha Campus, Chon Buri 20230, Thailand
关键词
Multiple traffic sign detection; Artificial bee colony algorithm; Midpoint circle algorithm; Color segmentation; Intelligent transportation systems; IMAGE SEGMENTATION; ALGORITHM; VEHICLES;
D O I
10.1007/s12530-017-9215-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Traffic signs play an important role in warning drivers by providing information about traffic restrictions, directions, and road quality in order to make sure that every driver is kept safe. Over the past decade, the development of autonomous vehicles has been an active area of research. Therefore, automatic traffic sign detection is a crucial part of the intelligent transportation systems that can be used in autonomous vehicles to detect traffic signs on the road. The optimization methods based on a biologically inspired computation are very powerful in solving optimization problems. In this work, we consider the traffic sign detection task as an optimization problem and propose the artificial bee colony (ABC) method, one of the most popular biologically inspired methods, as an alternative approach for solving it. In other words, we aim to present an algorithm for the automatic detection of multiple traffic signs with a circular shape based on solutions generated by the ABC method without considering the conventional Hough transform principles. Experimental results obtained by our method demonstrate that the proposed approach works well for multiple traffic sign detection and outperforms other existing algorithms.
引用
收藏
页码:255 / 264
页数:10
相关论文
共 32 条
[1]  
ABDI L., 2017, P S APPL COMP APR, P131
[2]   Hybrid ABC-ANN for pavement surface distress detection and classification [J].
Banharnsakun, Anan .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2017, 8 (02) :699-710
[3]  
Banharnsakun A, 2012, IEEE SYS MAN CYBERN, P1610, DOI 10.1109/ICSMC.2012.6377967
[4]   On circular traffic sign detection and recognition [J].
Berkaya, Selcan Kaplan ;
Gunduz, Huseyin ;
Ozsen, Ozgur ;
Akinlar, Cuneyt ;
Gunal, Serkan .
EXPERT SYSTEMS WITH APPLICATIONS, 2016, 48 :67-75
[5]   Change Detection in Satellite Images Using a Genetic Algorithm Approach [J].
Celik, Turgay .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2010, 7 (02) :386-390
[6]   Multi-circle detection on images using artificial bee colony (ABC) optimization [J].
Cuevas, Erik ;
Sencion-Echauri, Felipe ;
Zaldivar, Daniel ;
Perez-Cisneros, Marco .
SOFT COMPUTING, 2012, 16 (02) :281-296
[7]  
Escalera S., 2011, Springer, Traffic-Sign Recognition Systems, SpringerBriefs in Computer Science 2011, P5
[8]  
Flanders Harley, 2014, CALCULUS ANAL GEOMET
[9]  
García-Garrido MA, 2005, LECT NOTES COMPUT SC, V3643, P543, DOI 10.1007/11556985_71
[10]   A Review of Motion Planning Techniques for Automated Vehicles [J].
Gonzalez, David ;
Perez, Joshue ;
Milanes, Vicente ;
Nashashibi, Fawzi .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (04) :1135-1145