EMVD: Efficient Multitype Vehicle Detection Algorithm Using Deep Learning Approach in Vehicular Communication Network for Radio Resource Management

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Vartika Agarwal 1,* Sachin Sharma 1

1. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2022.02.03

Received: 10 Dec. 2021 / Revised: 6 Jan. 2022 / Accepted: 26 Feb. 2022 / Published: 8 Apr. 2022

Index Terms

FRCNN, Vehicular communication network, Radio Resource Management, Real Time Traffic Database, Vehicle to Vehicle communication.


Radio resource allocation in VCN is a challenging role in an intelligent transportation system due to traffic congestion. Lot of time is wasted because of traffic congestion. Due to traffic congestion, user have to miss their important work. In this paper, we propose radio resource allocation scheme so that user can utilize their time by taking the advantage of subscription plan. In this scenario, multitype vehicle identification scheme from real time traffic database is proposed, its history will match in transport database and vehicle travelling history database. Proposed method indicates 95% accuracy for multitype vehicle detection. Subscription plans are allocated to the user on the basis of resource allocation, scheduling, levelling and forecasting. This scheme is better for traffic management, vehicle tracking as well as time management.

Cite This Paper

Vartika Agarwal, Sachin Sharma, " EMVD: Efficient Multitype Vehicle Detection Algorithm Using Deep Learning Approach in Vehicular Communication Network for Radio Resource Management", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.2, pp. 25-37, 2022. DOI: 10.5815/ijigsp.2022.02.03


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