Cloud Computing-Based Vehicle Image Recognition Systems for Intelligent Traffic Management: A Systematic Review
DOI:
https://doi.org/10.29070/9nk2mn45Keywords:
Cloud Computing Based, Vehicle, Image Recognition, Intelligent Traffic ManagementAbstract
Urbanization, more vehicles on the road, and the resulting congestion have posed huge problems for transportation authorities in ensuring efficient and safe traffic control on the roads. The recent development of cloud computing and artificial intelligence technologies has paved the way for the creation of intelligent traffic management systems that can handle and analyze massive amounts of vehicle image information in real time. This systematic review summarizes the existing work performed on cloud computing based vehicle image recognition system for intelligent traffic management and highlights the status of research on cloud computing architectures, computer vision techniques and machine learning algorithms. It examines the development of the vehicle image recognition technologies, such as the convolutional neural networks, object detection models, and deep learning frameworks, and their integration with cloud platforms for scalable and high-performance traffic monitoring applications. In addition, it examines the benefits of edge computing, IoT devices, and distributed cloud systems for boosting processing power, minimizing latency, and increasing system reliability. It also covers important application areas like incident detection, congestion prediction, traffic flow analysis, licence plate recognition, vehicle detection and classification. It also highlights key areas of concern for data privacy, cyber security, computational expenses, interoperability, and real-time performance. Last but not least, the study points out the existing research gaps and further directions to improve the secure, scalable, and intelligent design of vehicle image recognition systems in the cloud for sustainable urban mobility and next-generation smart transportation infrastructures.
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