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

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.