An Intelligent Vehicle Detection and Recognition Framework for Traffic Cyber-Physical Systems
DOI:
https://doi.org/10.22399/ijcesen.3180Keywords:
Vehicle Detection, Vehicle Recognition, Traffic Monitoring, Cyber-Physical Systems (CPS), Intelligent Transportation Systems (ITS)Abstract
The rapid growth of urban traffic has necessitated the integration of intelligent systems within Cyber-Physical Systems (CPS) for real-time monitoring and management. This paper presents an efficient approach to vehicle detection and recognition tailored for Traffic Cyber-Physical Systems (CPS), aimed at enhancing situational awareness, safety, and traffic flow optimization. Leveraging a combination of computer vision techniques and deep learning models, the proposed framework accurately detects vehicles in diverse environmental conditions and recognizes their types and license plates. The system incorporates real-time image acquisition from roadside cameras, pre-processing pipelines, and a convolutional neural network (CNN)-based architecture for robust vehicle classification and identification. Experimental results demonstrate high accuracy and reliability across various traffic scenarios, including low-light and high-occlusion conditions. The proposed model is scalable, adaptable to edge-based deployment, and contributes significantly to the development of responsive and intelligent traffic CPS infrastructure. This research lays the foundation for more comprehensive intelligent transportation systems, facilitating autonomous decision-making and efficient traffic control.
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