Efficient vehicle detection and traffic control managememt using YOLOv5 - a simulation-based approach

Khalifa, Othman Omran and Mohd Daud, Hariz Naufal and Ahmed, Muhammed Zaharadeen and Hassan Abdalla Hashim, Aisha and Esgiar, Abdelrahim Nasser (2024) Efficient vehicle detection and traffic control managememt using YOLOv5 - a simulation-based approach. PERINTIS eJournal, 14 (2). pp. 27-38. E-ISSN 2232-0725

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Abstract

Soon, the technology for self-driving vehicles will occupy the center stage in automotive engineering. Implementing such technologies can stimulate driving accuracy and reduce the rate of accidents caused by human error. Humans are prone to limitations such as fatigue, lack of focus, or boredom, which are significant contributors to accidents on roads and highways. This paper presents the feasibility of implementing smart cities to achieve efficient vehicle distinction and traffic control management using the concept of deep learning. This process involves training devices to adapt and exhibit the capability to distinguish realistic scene situations using simulation for validation. The YOLOv5 simulation tool is utilized due to its efficiency in generating the best Mean Average Precision (MAP). The findings reveal that the YOLOv5 model achieves a MAP of 60.36% with 50% recall and operates at 58 frames per second, demonstrating superior precision and efficiency compared to existing object detection

Item Type:	Article (Journal)
Uncontrolled Keywords:	Autonomous Driving, Deep Learning, Smart Cities, Object Detection.
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	Prof. Dr Othman O. Khalifa
Date Deposited:	14 Jan 2025 12:09
Last Modified:	14 Jan 2025 12:09
URI:	http://irep.iium.edu.my/id/eprint/117911

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