Vehicle logo recognition using whitening transformation and deep learning

Soon, Foo Chong and Khaw, Hui Ying and Chuah, Joon Huang and Kanesan, Jeevan (2019) Vehicle logo recognition using whitening transformation and deep learning. Signal, Image and Video Processing, 13 (1). pp. 111-119. ISSN 1863-1703

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Official URL: https://doi.org/10.1007/s11760-018-1335-4

Abstract

This paper presents a vehicle logo recognition using a deep convolutional neural network (CNN) method and whitening transformation technique to remove redundancy of adjacent image pixels. Backpropagation algorithm with stochastic gradient descent optimization technique has been deployed to train and obtain weight filters of the networks. Seven layers of our proposed CNN incorporating an input layer, five hidden layers and an output layer have been implemented to capture rich and discriminative information of vehicle logo images. Functioning as the output layer of the network, the softmax classifier is utilized to handle multiple classes of vehicle logo image. For a given vehicle logo image, the network provides the probability for each vehicle manufacturer to which the given logo image belongs. Unlike most of the common traditional methods that employ handcrafted visual features, our proposed method is able to automatically learn and extract high-level features for the classification task. The extracted features are discriminative sufficiently to perform well in various imaging conditions and complex scenes. We validate our proposed method by utilizing a public vehicle logo image dataset, which comprises 10,000 and 1500 vehicle logo images for training and validation objective, respectively. Experimental results based on our proposed method outperform other existing methods in terms of the computational cost and overall classification accuracy of 99.13%. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

Item Type: Article
Uncontrolled Keywords: Convolutional neural network; Deep learning; Optimization; Vehicle logo recognition; Whitening transformation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 13 Jan 2020 08:07
Last Modified: 13 Jan 2020 08:07
URI: http://eprints.um.edu.my/id/eprint/23398

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