Open Access

ARTICLE

A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

by Adi Alhudhaif^1,*, Ammar Saeed², Talha Imran², Muhammad Kamran³, Ahmed S. Alghamdi³, Ahmed O. Aseeri¹, Shtwai Alsubai¹

1 Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj, 11942, Saudi Arabia
2 Department of Computer Science, COMSATS University Islamabad, Wah Cantt, 47010, Pakistan
3 University of Jeddah, College of Computer Science and Engineering Department of Cybersecurity, Jeddah, 21959, Saudi Arabia

* Corresponding Author: Adi Alhudhaif. Email:

Computer Systems Science and Engineering 2022, 40(1), 223-235. https://doi.org/10.32604/csse.2022.018430

Received 09 March 2021; Accepted 30 April 2021; Issue published 26 August 2021

Abstract

Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme, particle swarm optimization (PSO), was employed for autonomous vehicle classification. The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented. The trained model was classified using several classifiers; however, the Cubic SVM (CSVM) classifier was found to outperform the others in both time consumption and accuracy (94.8%). The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accuracy (94.8%) but also in terms of training time (82.7 s) and speed prediction (380 obs/sec).

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