Author
Listed:
- 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
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248007, India)
- Rajesh Singh
(Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, CP, Mexico)
- 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
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, CP, Mexico)
- Ahmed Saeed AlGhamdi
(Department of Computer Engineering, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21994, Saudi Arabia)
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.
Suggested Citation
Sudhir Kumar Rajput & Jagdish Chandra Patni & Sultan S. Alshamrani & Vaibhav Chaudhari & Ankur Dumka & Rajesh Singh & Mamoon Rashid & Anita Gehlot & Ahmed Saeed AlGhamdi, 2022.
"Automatic Vehicle Identification and Classification Model Using the YOLOv3 Algorithm for a Toll Management System,"
Sustainability, MDPI, vol. 14(15), pages 1-15, July.
Handle:
RePEc:gam:jsusta:v:14:y:2022:i:15:p:9163-:d:872271
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Cited by:
- Zepeng Gao & Jianbo Feng & Chao Wang & Yu Cao & Bonan Qin & Tao Zhang & Senqi Tan & Riya Zeng & Hongbin Ren & Tongxin Ma & Youshan Hou & Jie Xiao, 2022.
"Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction,"
Sustainability, MDPI, vol. 15(1), pages 1-18, December.
- Junli Liu & Xiaofeng Liu & Qiang Chen & Shuyun Niu, 2023.
"A Traffic Parameter Extraction Model Using Small Vehicle Detection and Tracking in Low-Brightness Aerial Images,"
Sustainability, MDPI, vol. 15(11), pages 1-23, May.
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