What is it about?

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.

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Why is it important?

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%.


A high-performance approach for accurate vehical logo classification.

Dr Joon Huang Chuah
University of Malaya

Read the Original

This page is a summary of: Vehicle logo recognition using whitening transformation and deep learning, Signal Image and Video Processing, July 2018, Springer Science + Business Media, DOI: 10.1007/s11760-018-1335-4.
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