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@article{179672,
        author = {Prakasika K and Srisathiya K and Sivapriya P and Monisha C and Manusri V},
        title = {AI Powered Traffic Management System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7841-7847},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179672},
        abstract = {This project presents an integrated system combining computer vision and embedded hardware for intelligent traffic enforcement and automated vehicle speed regulation. The system is divided into two main stages: software and hard- ware. On software side, an advanced object detection model (YOLOv8) is used to identify and extract license plate numbers and helmet-wearing status from uploaded images. Simultaneously, a trained deep learning model identifies road speed signs (20km/h, 50km/h, 100km/h) using real-time webcam input. The extracted license plate data and detected speed limits are then transmitted to an Arduino Uno via serial communication. On hardware side, the Arduino processes the received data to display the license plate on a 16x2 LCD. Based on the detected speed limit, the system uses a MOSFET circuit to control the speed of a DC motor accordingly. Additionally, a buzzer and LED indicators provide visual and auditory alerts based on helmet compliance and speed instructions. This intelligent system simulates a real-world scenario of smart surveillance and automated vehicle response, promoting road safety and law enforcement through automation. It can be further expanded for real-time vehicle tracking and traffic rule violation detection in smart city applications.},
        keywords = {ALPR, 16x2 LCD, Detected Speed Limit, MOS- FET for controlling the Speed of Dc Motor, Visual and Auditory alerts based on Helmet Compliance},
        month = {May},
        }