A Systematic Review of Drone Based Road Traffic Monitoring System

Bisio, Igor; Garibotto, Chiara; Haleem, Halar; Lavagetto, Fabio; Sciarrone, Andrea

doi:10.1109/access.2022.3207282

Cited by 42 publications

(17 citation statements)

References 135 publications

(261 reference statements)

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“…In urban intelligent transportation systems, pedestrian detection and recognition are crucial tasks to ensure pedestrian traffic safety and enhance traffic efficiency [8,9]. Researchers have proposed many deep learning-based pedestrian detection and recognition models.…”

Section: Pedestrian Detection and Recognitionmentioning

confidence: 99%

YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety

Jun,

Lai,

et al. 2024

Preprint

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With the rapid development of urbanization, the role of urban transportation systems has become increasingly prominent. However, traditional methods of traffic management are struggling to cope with the growing demands of traffic and the complexity of urban environments. In response to this situation, we propose the YOLOv8-BCC algorithm to address existing shortcomings. Leveraging advanced technologies such as CFNet, CBAM attention modules, and BIFPN structure, our algorithm aims to enhance the accuracy, real-time performance, and adaptability of urban traffic intelligent detection systems. Experimental results demonstrate significant improvements in detection accuracy and real-time performance compared to traditional methods. The introduction of the YOLOv8-BCC algorithm provides a robust solution for enhancing urban traffic safety and intelligent management.

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Section: Pedestrian Detection and Recognitionmentioning

confidence: 99%

YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety

Jun,

Lai,

et al. 2024

Preprint

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“…They evaluated the system on a dataset that contained 8000 images and reached 93.5% accuracy. Bisio et al [108] have published an extensive review of drone-based traffic monitoring systems. The work of Liu et al [109] also uses computer vision to detect congestion, but their work relies on large and medium cities' public security cameras and their algorithm reaches up to 82.5% accuracy.…”

Section: Infrastructure Levelmentioning

confidence: 99%

Machine Learning Use-Cases in C-ITS Applications

Bereczki¹,

Simon²

2023

Infocommunications journal

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In recent years, the development of Cooperative Intelligent Transportation Systems (C-ITS) have witnessed significant growth thus improving the smart transportation concept. The ground of the new C-ITS applications are machine learning algorithms. The goal of this paper is to give a structured and comprehensive overview of machine learning use-cases in the field of C-ITS. It reviews recent novel studies and solutions on CITS applications that are based on machine learning algorithms. These works are organised based on their operational area, including self-inspection level, inter-vehicle level and infrastructure level. The primary objective of this paper is to demonstrate the potential of artificial intelligence in enhancing C-ITS applications.

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“…However, to the best of our knowledge, there is a lack of systematic summaries on this topic. Although several review papers on UAV applications have been published, they mainly focus on specific application areas (e.g., urban flood management [14], urban building management [15], [16], urban traffic management disaster [17], urban ecological environment monitoring [18], etc.) without focusing on the entire business process and technical system.…”

Section: ) In Terms Of Cost and Work Efficiency Consumer-gradementioning

confidence: 99%

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

Xu,

Wang,

Han

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Smart management of urban space is an important way to achieve the goal of sustainable urban development. Unmanned aerial vehicles (UAVs) have been widely used in urban space management work and have greatly improved the level of it. UAV technology assisting urban space management involves many technical details and has generated a number of problems. Therefore, a systematic review of the applications of UAVs in urban spatial management work is very necessary and can provide a comprehensive reference for relevant researchers. This is conducive to the generation of more new insights, methods, and applications. However, according to our research, there is a lack of relevant systematic investigation. We screened and researched a large number of relevant literatures (about 230) with the help of search tools such as Web of Science and IEEE Explore, and combined with our own working experience to review and summarize the field in an all-round way. Taking the definition, needs, and challenges of urban spatial management as an entry point, this paper systematically summarizes the task flow, working paradigm, technical system, and application direction of UAV-assisted urban spatial management. It also takes our recently developed UAV urban management system as an example to provide an introduction on how to integrate advanced technologies such as UAV and artificial intelligence. Finally, this paper summarizes several concluding findings and proposes several future research directions. The research in this paper shows that UAV technology has already played a great role in urban spatial management work, but it is still far from realizing automation and intelligence, and it needs to continue to make efforts in terms of methods, systems, applications, and policies. In order to achieve these goals, UAV technology can be further integrated with advanced technologies such as deep learning, more types of UAV industry applications can be carried out, more functional UAS can be developed, and better management policies can be formulated.

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A Systematic Review of Drone Based Road Traffic Monitoring System

Cited by 42 publications

References 135 publications

YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety

YOLOv8-BCC: Lightweight Object Detection Model Boosts Urban Traffic Safety

Machine Learning Use-Cases in C-ITS Applications

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

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