Investigation of convolutional neural network model for vehicle classification in smart city

Ismail, Ahsiah and Ismail, Amelia Ritahani and Shaharuddin, Nur Azri and Ahmad Puzi, Asmarani and Awang, Suryanti (2025) Investigation of convolutional neural network model for vehicle classification in smart city. International Journal of Advanced Computer Science and Applications, 16 (4). pp. 905-911. ISSN 2158107X

	PDF - Published Version Restricted to Registered users only Download (884kB) | Request a copy
	PDF - Supplemental Material Restricted to Registered users only Download (189kB) | Request a copy

Abstract

Smart city optimize efficiency by integrating advanced digital technologies, real-time data analytics, and intelligent automation. With the evolution of big data, smart cities enhance infrastructure and provide intelligent solutions for transportation with the integration of high-level adaptability of computer technologies including artificial intelligence (AI). The optimization can be achieved through predictive analytics in providing intelligent solutions for transportation. However, this requires reliable and accurate informative data as input for predictive analytics. Therefore, in this paper, five models of Convolutional Neural Network (CNN) deep learning method are investigated to determine the most accurate model for classification; namely Single Shot Detector (SSD) Resnet50, SSD Resnet152, SSD MobileNet, You Only Look Once (YOLO) YOLOv5 and YOLOv8. A total of 1324 vehicle images are collected to test these CNN models. The images consist of five different categories of vehicles, which are ambulance, car, motorcycle, bus and truck. The performances of all the models are compared. From the evaluation, the model YOLOv8 attained 0.956 of precision, 0.968 of recall and 0.968 of F1 score and outperformed the others. In terms of computational time, YOLOv5 is the fastest. However, a minimal computational time difference is observed between the YOLOv5 and YOLOv8, which were separated by only 20 minutes.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Vehicle classification; convolutional neural network; SSD; YOLO; MobileNets
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr AHSIAH ISMAIL
Date Deposited:	23 May 2025 15:15
Last Modified:	23 May 2025 15:24
URI:	http://irep.iium.edu.my/id/eprint/121167

Actions (login required)

View Item