A Comparative Analysis of Deep Learning based Vehicle Detection Approaches

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Published Mar 31, 2023
https://doi.org/10.47164/ijngc.v14i2.976
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Volume 14, Issue 2, March 2023

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Nikita Singhal
Department of Computer Engineering , SIRT, Sage University
Dr Lalji Prasad
Department of Computer Engineering , SIRT, Sage University

Abstract

Numerous traffic-related problems arise as a result of the exponential growth in the number of vehicles on the road. Vehicle detection is important in many smart transportation applications, including transportation planning, transportation management, traffic signal automation, and autonomous driving. Many researchers have spent a lot of time and effort on it over the last few decades, and they have achieved a lot. In this paper, we compared the performances of major deep learning models: Faster RCNN, YOLOv3, YOLOv4, YOLOv5, and SSD for vehicle detection with variable image size using two different vehicle detection datasets: Highway dataset and MIOTCD. The datasets that are most commonly used in this domain are also analyzed and reviewed. Additionally, we have
emphasized the opportunities and challenges in this domain for the future.

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Nikita Singhal, Department of Computer Engineering , SIRT, Sage University

Nikita Singhal is an Assistant Professor in the Department of Computer
Engineering, Army Institute of Technology, Pune. She is pursuing her PhD in CSE from SAGE
University, Indore and received her MTech in Computer Science and Engineering from Defence
Institute of Technology (DU), Pune. She has more than ten years of academic and research
experience. Her research interests include deep learning, image processing and computer
networks security.

Dr Lalji Prasad, Department of Computer Engineering , SIRT, Sage University

Lalji Prasad is a Professor of Computer Science and Engineering at SAGE University, Indore. He
received his PhD in Computer Science and Engineering from the Rajiv Gandhi Proudyogiki
Vishwavidyalaya in Bhopal, India, and ME in Software Engineering from IET DAVV. He has
more than 20 years of academic and R&D experience. He is a reviewer in various reputed
journals. His research interests are in the areas of computer vision, deep learning and, software
engineering.

How to Cite
Nikita Singhal, & Lalji Prasad. (2023). A Comparative Analysis of Deep Learning based Vehicle Detection Approaches. International Journal of Next-Generation Computing, 14(2). https://doi.org/10.47164/ijngc.v14i2.976
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Europe PMC

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