“Amalgam Based Intelligent Road Surveillance Using Machine Learning Algorithm.”

With the growing volume of vehicles on roads, there is a rising need for intelligent systems that can help in real-time traffic monitoring and enforcement. This paper introduces a practical and efficient solution that combines several techniques—vehicle detection, license plate recognition, and colour identification—under one, unified framework. We employ YOLOv8 for precise object detection, Haar Cascade for license plate identification, and Easy OCR to extract registration numbers from them. For vehicle colour identification, the HSV colour space is employed for enhanced classification under varying lighting. The system records video input via a Tapo C310 camera and processes the frames in real time at 30 frames per second. Each module is standalone, meaning that the system can be easily scaled and is always stable even when it is performing intensive processing. We applied our system on a dataset of 500 images and established that it recorded 98.5