Traffic Sign Classification based on Neural Network for Advance Driver Assistance System

• Autorzy: Hannan M. A. , Wali S. B. , Pin T. J. , Hussain A. , Samad S. A.

Identyfikatory

DOI

10.12915/pe.2014.11.44

Warianty tytułu

PL

Rozpoznawanie i klasyfikacja znaków drogowych z wykorzystaniem sieci neuronowych

• Języki publikacji: EN

Abstrakty

EN

Traffic sign is utmost important information or rule in transportation. In order to ensure the transportation safety the automotive industry has developed Advance Driver Assistance System (ADAS). Among the ADAS system, development of TSDR is the most challenging to the researchers and developers due to unsatisfying performance. This paper deals with, automatic traffic sign classification and reduces the effect of illumination and variable lighting over the classification scheme by using neural network according to the traffic sign shape. There are three main phase of the classification scheme such as; pre-processing using image normalization, feature extraction using color information of 16-point pixel values and multilayer feed forward neural network for classification. An accuracy rate of 84.4% has been achieved by the proposed system. Overall processing time of 0.134s shows the system is a fast system and real-time application.

PL

W artykule opisano metodę automatycznego rozpoznawania I klasyfikacji znaków drogowych z przenaczeniem do inteligentnych systemów wspomagania kierowcy ADAS. Do tego celu wykorzystano sieci neuronowe przeprowadzając normalizację obrazu, ekstrakcję cech i klasyfikację. Osiągnieto dokładność rozpoznawania rzędu 84% przy przeciętnym czasie rozpoznawania około 0.13 s.

Słowa kluczowe

EN

traffic sign classification; advanced driver assistance system; image normalization; neural network

PL

rozpoznawanie znaków drogowych; sieci neuronowe; system ADAS

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2014
• Tom: R. 90, nr 11
• Strony: 169--172
• Opis fizyczny: Bibliogr. 21 poz., il., schem., tab., wykr.

Twórcy

autor

Hannan M. A.

• Dept. of Electrical, Electronic & Systems Engineering Universiti Kebangsaan Malaysia, Malaysia

autor

Wali S. B.

• Dept. of Electrical, Electronic & Systems Engineering Universiti Kebangsaan Malaysia, Malaysia

autor

Pin T. J.

• Dept. of Electrical, Electronic & Systems Engineering Universiti Kebangsaan Malaysia, Malaysia

autor

Hussain A.

• Dept. of Electrical, Electronic & Systems Engineering Universiti Kebangsaan Malaysia, Malaysia

autor

Samad S. A.

• Dept. of Electrical, Electronic & Systems Engineering Universiti Kebangsaan Malaysia, Malaysia

Bibliografia

• [1] Hussain A., Hannan M.A., Mohamed A., Sanusi H., Ariffin A.K., Vehicle crash analysis for airbag deployment decision, International journal of automotive technology, 7 (2006), No 2, 179-185.
• [2] Hannan M.A., Hussain A., Mohamed A., Samad S.A., Development of an embedded vehicle safety system for frontal crash detection, International Journal of Crashworthiness, 13 (2008) No. 5, 579-587.
• [3] Hannan M.A., Hussain A., Samad S.A., Mohamed A., Wahab D.A., Ihsan K.A.M., Development of an intelligent safety system for occupant detection, classification and position, Int. J. Automotive. Technology, 7 (2006), 827-832.
• [4] Irmak H., Real Time Traffic Sign Recognition System on FPGA, Thesis M.S. Middle East Technical University, Turkey (2009).
• [5] Wang, W., Wei, C.H., Zhang, L. & Wang, X. 2012. Traffic-Signs Recognition System Based on Multi-Features, IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2012), p.p. 120-123.
• [6] Escalera A.D.L, Armingol J.M. & Mata M., Traffic Sign Recognition and Analysis for Intelligent Vehicles, Image and Vision Computing, (2003), 21
• [7] Sheng Y., Zhang K., Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks, Optical and Digital Image Processing, 7000 (2008), 70001I-70001I-12.
• [8] Gudigar A., Jagadale B.N., Mahesh, P.K., Raghavendra U, Kernel Based Automatic Traffic Sign Detection and Recognition Using SVM, Eco-Friendly Computing and Communication Systems, (2012), 153–161.
• [9] Bascon S.M., Rodriguez J.A., Arroyo S.L., Caballero A.F., Lopez-Ferreras F., An optimization on pictogram identification for the road-sign recognition task using SVMs, Computer Vision and Image Understanding, 114 (2010) No. 3, 373–383.
• [10] Zaklouta F., Stanciulescu B., Real-time traffic sign recognition in three stages, Robotics and Autonomous Systems, 62 (2014), No. 1, 16–24.
• [11] Kaplan K., Kurtul C. & Akin H.L., Real-Time Traffic Sign Detection and Classification Method for Intelligent Vehicles, IEEE International Conference on Vehicular Electronics and Safety, (2012) 448-453.
• [12] Wali S.B., Javadi M.S., Hannan M.A. & Abdullah S., Traffic Sign Detection Based On Shape Matching And Color Segmentation For Intelligent Vehicles, International Conference on Engineering and Built Environment (ICEBE), ID:105, 2012.
• [13] Qin F., Fang B. & Zhao H.J., Traffic Sign Segmentation and Recognition in Scene Images, Chinese Conference on Pattern Recognition (CCPR), (2010) 1-5.
• [14] Saric, M., H. Dujmic, and M. Russo. Scene Text Extraction in HSI Color Space using K-means Algorithm and Modified Cylindrical Distance. Przegląd Elektrotechniczny 89 (2013), 117-121.
• [15] Pascual J.P.C., Advanced Driver Assistance System based on Computer Vision using Detection, Recognition and Tracking of Road Signs, Thesis Ph.D. Universidad Carlos III de Madrid, Spain, (2009).
• [16] Gonzalez R.C. & Woods R.E., Digital Image Processing. 2nd Edition. New Jersey: Prentice Hall, (2002).
• [17] Available: [Online] http://www.cvl.isy.liu.se/research/traffic-signs-dataset/download
• [18] Larsson F. & Felsberg M., Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition, In Proceedings of the 17th Scandinavian Conference on Image Analysis, SCIA 2011, LNCS 668, (2011) 238-249.
• [19] Transport Styrelsen. 2010. Markings (online) http://www.transportstyrelsen.se/sv/Vag/Vagmarken/ (10 January 2013)
• [21] Siyan Y., Xiaoying W., Qiguang M., Traffic-sign segmentation and recognition from natural scenes, IEEE Int. Conf. on Signal Processing, Communication and Computing (ICSPCC), (2011) 1-4