AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (991.7 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Review | Open Access

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

Jiaqi WangJian Su( )
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Show Author Information

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.

References

【1】
【1】
 
 
Computers, Materials & Continua
Pages 125-152

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Wang J, Su J. A Review of Object Detection Techniques in IoT-Based Intelligent Transportation Systems. Computers, Materials & Continua, 2025, 84(1): 125-152. https://doi.org/10.32604/cmc.2025.064309

2634

Views

113

Downloads

8

Crossref

2

Web of Science

8

Scopus

Received: 11 February 2025
Accepted: 08 April 2025
Published: 09 June 2025
© The Author 2024.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.