Automatic Vehicle Identification and Classification Model Using the YOLOv3 Algorithm for a Toll Management System

Sudhir Kumar Rajput, Jagdish Chandra Patni, Sultan S. Alshamrani, Vaibhav Chaudhari, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Anita Gehlot and Ahmed Saeed AlGhamdi
Additional contact information
Sudhir Kumar Rajput: School of Computer Science, UPES, Dehradun 248007, India
Jagdish Chandra Patni: School of Computer Science, UPES, Dehradun 248007, India
Sultan S. Alshamrani: Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Vaibhav Chaudhari: Department of Computer Science and Information Systems, BITS Pilani K.K. Birla Goa Campus, Sancoale 403001, India
Ankur Dumka: Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, India
Rajesh Singh: Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Mamoon Rashid: Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411048, India
Anita Gehlot: Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Ahmed Saeed AlGhamdi: Department of Computer Engineering, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21994, Saudi Arabia

Sustainability, 2022, vol. 14, issue 15, 1-15

Abstract: Vehicle identification and classification are some of the major tasks in the areas of toll management and traffic management, where these smart transportation systems are implemented by integrating various information communication technologies and multiple types of hardware. The currently shifting era toward artificial intelligence has also motivated the implementation of vehicle identification and classification using AI-based techniques, such as machine learning, artificial neural network and deep learning. In this research, we used the deep learning YOLOv3 algorithm and trained it on a custom dataset of vehicles that included different vehicle classes as per the Indian Government’s recommendation to implement the automatic vehicle identification and classification for use in the toll management system deployed at toll plazas. For faster processing of the test videos, the frames were saved at a certain interval and then the saved frames were passed through the algorithm. Apart from toll plazas, we also tested the algorithm for vehicle identification and classification on highways and urban areas. Implementing automatic vehicle identification and classification using traditional techniques is a highly proprietary endeavor. Since YOLOv3 is an open-standard-based algorithm, it paves the way to developing sustainable solutions in the area of smart transportation.

Keywords: smart transportation; vehicle identification; vehicle classification; YOLOv3 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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