What is it about?

This study aims to minimise the user variability in training CNN by automatically searching and optimising the CNN architecture, particularly in the field of vehicle logo recognition system. For this purpose, the architecture and hyper-parameters of CNN were selected according to the implementation of the stochastic method of particle swarm optimisation on the training–testing data. After obtaining the optimised hyper-parameters, the CNN is fine-tuned and trained to ensure better network convergence and classification performance.

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Why is it important?

In this study, a total of 14,950 vehicle logo images are divided into two independent training and testing sets. In addition, these images are segmented coarsely, thus the requirement of precise logo segmentation is obviated in this work. The learned features of the CNN were sufficiently discriminative to be classified using multiclass Softmax classifier. With implementation using a graphics processing unit (GPU), the computation time of the proposed method is acceptable for real-time application. The experimental results explicitly prove that the authors’ approach outperforms most of the state-of-the-art methods, achieving an accuracy of 99.1% over 13 vehicle manufacturers.


A highly accurate method for vehicle logo recognition.

Dr Joon Huang Chuah
University of Malaya

Read the Original

This page is a summary of: A Hyper-parameters Optimization of Deep Convolutional Neural Network Architecture for Vehicle Logo Recognition , IET Intelligent Transport Systems, July 2018, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-its.2018.5127.
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