Signal, Image and Video Processing

, Volume 13, Issue 1, pp 111–119 | Cite as

Vehicle logo recognition using whitening transformation and deep learning

  • Foo Chong Soon
  • Hui Ying Khaw
  • Joon Huang ChuahEmail author
  • Jeevan Kanesan
Original Paper


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%.


Vehicle logo recognition Deep learning Convolutional neural network Optimization Whitening transformation 



This work was financially supported by the Research Fund Assistance (BKP) Grant from the University of Malaya with the Grant Number BK044-2013.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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