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Content Provider | IET Digital Library |
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Author | Soon, Foo Chong Khaw, Hui Ying Chuah, Joon Huang Kanesan, Jeevan |
Abstract | The training of deep convolutional neural network (CNN) for classification purposes is critically dependent on the expertise of hyper-parameters tuning. 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. 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. |
Starting Page | 939 |
Ending Page | 946 |
Page Count | 8 |
ISSN | 1751956X |
Volume Number | 12 |
e-ISSN | 17519578 |
Issue Number | Issue 8, Oct (2018) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/12/8 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5127 |
Journal | IET Intelligent Transport Systems |
Publisher Date | 2018-07-06 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Classification Performance Classification Purposes Computation Time Computer Vision And Image Processing Technique Deep CNN Architecture Deep Convolutional Neural Network Architecture Feedforward Neural Network Graphics Processing Unit Graphics Processing Units Hyper-Parameter Optimisation Hyper-Parameter Tuning Image Classification Image Recognition Image Segmentation Intelligent Transport System Microprocessor Chips Microprocessors And Microcomputer Multiclass Softmax Classifier Network Convergence Neural Computing Technique Object Recognition Optimisation Technique Particle Swarm Optimisation Precise Logo Segmentation Road Vehicle Statistics Stochastic Linearised SCUC Stochastic Method Testing Set Traffic Engineering Computing Training Set Training-testing Data Vehicle Logo Recognition System |
Content Type | Text |
Resource Type | Article |
Subject | Law Transportation Environmental Science Mechanical Engineering |
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