Open Access

ARTICLE

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Shabana Habib¹, Noreen Fayyaz Khan^2,*

1 Department of Information Technology, College of Computer, Qassim University, Buraidah, 51452, Saudi Arabia
2 Department of Computer Science, Islamia College University, Peshawar, Pakistan

* Corresponding Author: Noreen Fayyaz Khan. Email:

Computers, Materials & Continua 2021, 69(3), 3321-3335. https://doi.org/10.32604/cmc.2021.015504

Received 25 November 2020; Accepted 02 March 2021; Issue published 24 August 2021

Abstract

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.

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