Citation: Alghamdi, A.S.; Saeed, A.; Kamran, M.; Mursi, K.T.; Almukadi, W.S. Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms. Electronics 2023, 12, 280. https:// doi.org/10.3390/electronics12020280 Academic Editor: Gwanggil Jeon Received: 15 December 2022 Revised: 29 December 2022 Accepted: 2 January 2023 Published: 5 January 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms Ahmed S. Alghamdi 1 , Ammar Saeed 2 , Muhammad Kamran 1, * , Khalid T. Mursi 1 and Wafa Sulaiman Almukadi 1 1 Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia 2 Department of Computer Science, COMSATS University Islamabad, Wah Cantt 47010, Pakistan * Correspondence: mkkamran@uj.edu.sa Abstract: Vehicle classification is a challenging task in the area of image processing. It involves the classification of various vehicles based on their color, model, and make. A distinctive variety of vehicles belonging to various model categories have been developed in the automobile industry, which has made it necessary to establish a compact system that can classify vehicles within a complex model group. A well-established vehicle classification system has applications in security, vehicle monitoring in traffic cameras, route analysis in autonomous vehicles, and traffic control systems. In this paper, a hybrid model based on the integration of a pre-trained Convolutional Neural Network (CNN) and an evolutionary feature selection model is proposed for vehicle classification. The proposed model performs classification of eight different vehicle categories including sports cars, luxury cars and hybrid power-house SUVs. The used in this work is derived from Stanford car dataset that contains almost 196 cars and vehicle classes. After performing appropriate data preparation and preprocessing steps, feature learning and extraction is carried out using pre-trained VGG16 first that learns and extracts deep features from the set of input images. These features are then taken out of the last fully connected layer of VGG16, and feature optimization phase is carried out using evolution-based nature-inspired optimization model Genetic Algorithm (GA). The classification is performed using numerous SVM kernels where Cubic SVM achieves an accuracy of 99.7% and outperforms other kernels as well as excels in terns of performance as compared to the existing works. Keywords: convolutional neural network; fused deep earning; vehicle classification 1. Introduction The evolution of the modern era has had a significant impact on the automobile industry, which has progressed rapidly. Nowadays, vehicles of the same companies are being released with various colors, models, and physical attributes, making it difficult to differentiate them without having some prior knowledge about those models that makes developing a system that could perform vehicle classification an even bigger challenge. The emerging concept of smart cities relies on an intelligent traffic monitoring and classification system that could detect and surveil different vehicles for traffic rule obstruction, security, and emergency situations [1]. The ever-increasing demand, production and usage of vehicles of all kinds of makes, colors and models, it becomes very difficult for a human agent to perform vehicle monitoring, record keeping, surveillance and detection for any kind of obstruction [2]. Therefore, establishing an automated system that can discriminate between various vehicle types is necessary. A model like this could have applications in the area of security, smart traffic systems, self-driving vehicles for environmental understanding and collision avoidance, criminal activity reduction, and vehicle-type detection [3]. An intelligent traffic system could also assist in crime reduction and criminal activity tracking, given that most criminal activities involve the use of some kind of vehicle for Electronics 2023, 12, 280. https://doi.org/10.3390/electronics12020280 https://www.mdpi.com/journal/electronics