Performance comparison between ANN and PCA techniques for road signs recognition

Mohd Ali, Nursabillilah and Mohd Mustafah, Yasir and Alang Md Rashid, Nahrul Khair (2013) Performance comparison between ANN and PCA techniques for road signs recognition. Applied Mechanics and Materials, 393. pp. 611-616. ISSN 1660-9336

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Abstract

This study reports about a comparison in recognizing road signs between Neural Network and Principal Component Analysis (PCA). The road sign with circular, triangular, octagonal and diamond shapes have been used in this study. Two recognition systems to determine the classes of the road signs class were implemented which are based on Feed Forward Neural Network and Principal Component Analysis (PCA). The performance of the trained classifier using Scaled Conjugate Gradient (SCG) back propagation function in Neural Network and PCA technique were evaluated on our test datasets. The experiments show that the system using PCA has a higher accuracy as compared to Neural Network with a minimum of 94% classification rate of road signs.

Item Type:	Article (Journal)
Additional Information:	5107/35990
Uncontrolled Keywords:	Feature extraction; Neural network; Partial occlusion; Principal component analysis (PCA); Recognition
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Yasir Mohd Mustafah
Date Deposited:	10 Mar 2014 15:39
Last Modified:	10 Mar 2014 15:39
URI:	http://irep.iium.edu.my/id/eprint/35990

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