Traffic Light Detection Using Back Propagation Neural Networks in Thailand

Main Article Content

พิศณุ คูมีชัย


This paper proposes a traffic lights detection method using back propagation neural network at the day-time. In the first step, traffic lights image are extracted from RGB color image. Input RGB image is transformed into Ycbcr color space, then red and green color dominant regions are selected as candidates. And, the final step, back propagation neural network applied for red color recognizes. For experimental result, a 3-layered back propagation neural network is applied to detect and determine the parts of a traffic light useful for detection. Experiments the accuracy of the method is shown and is compared with that by the k-nearest neighbor classification.

Article Details

How to Cite
คูมีชัย พ. (2018). Traffic Light Detection Using Back Propagation Neural Networks in Thailand. NKRAFA JOURNAL OF SCIENCE AND TECHNOLOGY, 13, 67–72. Retrieved from
Research Articles


[1] V.Gradinescu, C.Gorgorin, R.Diaconescu, V.Cristea, and L.Iftode. Adaptive traffic lights using car-to-car communication” in VTC Spring. IEEE: 21–25, 2007. [Online]. Available:

[2] T.-H.Hwang, I.H.Joo and S.I.Cho. Detection of traffic lights for vision-based car navigation system. in PSIVT: 682–691, 2006.

[3] X.Liu, S.Zhu, and K.Chen. Method of Traffic Sign Segmentation Based on Color-Stadardization. International Conference on Intelligent Human-Machine Systems and Cybernetics, 978-0-7695-3752-8: 193-197, 2009.

[4] W.Y.Wu, T.C.Hsieh and C.S.Lai. Extracing Road Sign Using The Color Information. 26 CANAKKALE : WORLD ACAD SCI, ENG & TECH-WASET: 282-286, 2007.

[5] D.T.Pankaj and M.E.Patil. Recognition of traffic symbols using Kmeans and shape analysis. International Journal of Engineering Research and Technology (IJERT) 2(5): 162-169, 2013.

[6] A.Danti and Y.Kulkarni. Images Processing Approach To Detect Road Sign in India Roads. International Journal Of Research in Advent Technology, 1(5): 409-417, 2013.

[7] H.Fleyeh. Shadow And Highlight Invariant Colour Segmentation Algorithm For Traffic Signs. IEEE Conference on Cybernetics and Intelligent Systems. Bangkok, Thailand, 2006.

[8] A.Ruta, Y.Li and X.Liu. Detection, Tracking and Recognition of Traffic Signs from Video Input. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems. Beijing, China, 2008.

[9] A.Lorasakul and J.Suthakom. Traffic Sign Recognition for Intelligent Vehicle/Driver. In International Conference on Ubiquitos Robots and Ambient Intelligence, 2007.
[10] Mohammad A.N. Al-Azawi. Neural Network Based Automatic Traffic Signs Recognition. International Journal of Digital Information and Wireless Communications, 2012.

[11] H.N.Dean and K.V.Jabir. Real Time Detection and Recognition of Indian Traffic Signs using Matlab. International Journal of Scientific & Engineering Research, 4(5): 684-690, 2013.

[12] S.M.Baschon, S.L.Arroyo, P.G. Jimenez, H.G. Moreno and F.L.Ferreras. Road-Sign Detection and Recognition Based on Support Vector Machines. IEEE. Transactions On Intelligent Transportation System, 8(2), 2007.

[13] A.V.Deshpande. Design Approach for a Novel Traffic Sign Recognition System by Using LDA and Images Segmentation by Exploring the Color and Shape Feature of an Image. International Journal of Engineering Research and Applications, 4(11): 20-26, 2014.

[14] T.Boongoen, N.Iam-On and B.Undara. Improving Face Detection with Bi-Level Classification Model. RTAFA Journal of Science and Technology, 12(12): 52-63, 2016.