An Intelligent Vehicle Detection and Recognition Framework for Traffic Cyber-Physical Systems - Wei, Xiuling; Marloun P. Sejera (International Journal of Computational and Experimental Science and Engineering, 2025)
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.