Open Access

REVIEW

A Review of Object Detection Techniques in IoT-Based Intelligent Transportation Systems

Jiaqi Wang, Jian Su^*

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Jian Su. Email:

Computers, Materials & Continua 2025, 84(1), 125-152. https://doi.org/10.32604/cmc.2025.064309

Received 11 February 2025; Accepted 08 April 2025; Issue published 09 June 2025

Abstract

The Intelligent Transportation System (ITS), as a vital means to alleviate traffic congestion and reduce traffic accidents, demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things (IoT) technologies. The enhancement of its performance largely depends on breakthrough advancements in object detection technology. However, current object detection technology still faces numerous challenges, such as accuracy, robustness, and data privacy issues. These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions. This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS, aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems. To achieve this objective, we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to search, screen, and assess the eligibility of relevant literature, ultimately including 88 studies. Through an analysis of these studies, we summarized the characteristics, advantages, and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage. Additionally, we examined its applications in ITS from three perspectives: vehicle detection, pedestrian detection, and traffic sign detection. We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues. This review offers researchers a comprehensive perspective, identifying potential improvement directions for object detection technology in ITS, including accuracy, robustness, real-time performance, data annotation cost, and data privacy. In doing so, it provides significant guidance for the further development of IoT-based intelligent transportation systems.

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Keywords

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Supplementary Material

Supplementary Material File

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