Performance Comparison between ANN and PCA Techniques for Road Signs Recognition

Nursabillilah Mohd Ali; Yasir Mohd  Mustafah; Nahrul Khair Alang Md Rashid

doi:10.4028/www.scientific.net/AMM.393.611

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 393Performance Comparison between ANN and PCA...

Performance Comparison between ANN and PCA Techniques for Road Signs Recognition

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.

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Periodical:

Applied Mechanics and Materials (Volume 393)

Pages:

611-616

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.393.611

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Online since:

September 2013

Authors:

Nursabillilah Mohd Ali, Yasir Mohd Mustafah, Nahrul Khair Alang Md Rashid

Keywords:

Feature Extraction, Neural Network (NN), Partial Occlusion, Principal Component Analysis (PCA), Recognition

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