A Comprehensive Review on Object Detectors for Urban Mobility on Smart Traffic Management

Authors

  • Shivani Mistry  Research Scholar, Dept. of Computer Engineering, Sigma Institute of Engineering, Gujarat, India
  • Sheshang Degadwala  Associate Professor & Head of Department, Dept. of Computer Engineering, Sigma University, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT2361050

Keywords:

Urban mobility, smart traffic management, object detectors, comprehensive review, intelligent transportation systems, deep learning, computer vision.

Abstract

This comprehensive review explores the landscape of object detectors in the context of urban mobility for smart traffic management. With the increasing complexity of urban environments and the integration of intelligent transportation systems, the demand for accurate and efficient object detection algorithms has surged. This paper provides a thorough examination of state-of-the-art object detectors, evaluating their performance, strengths, and limitations in the specific context of urban mobility. The review encompasses a wide range of detectors, including traditional computer vision methods and modern deep learning approaches, discussing their applicability to real-world urban traffic scenarios. By synthesizing insights from diverse methodologies, this review aims to guide researchers, practitioners, and policymakers in selecting suitable object detectors for enhancing smart traffic management systems in urban settings.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

License

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Creative Commons License

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

How to Cite

[1]
Shivani Mistry, Sheshang Degadwala, " A Comprehensive Review on Object Detectors for Urban Mobility on Smart Traffic Management, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.295-300, September-October-2023. Available at doi : https://doi.org/10.32628/CSEIT2361050