Advanced Traffic Monitoring & Speed Regulation using YOLOVII
Loading...
Files
Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
New Horizon College of Engineering
Abstract
This work proposes an end-to-end real-time traffic monitoring and over-speed detection
system with deep learning and computer vision to improve road safety and automate the
process of traffic enforcement. Traditional speed detection methods—such as radar guns,
fixed speed cameras, and manual surveillance—suffer from scalability, high costs, and
limited coverage. In response, the proposed system will present a fully automated, vision
based framework that integrates high-accuracy vehicle detection, speed estimation, license
plate recognition, and automated alert delivery using modern AI techniques. At its core, the
system employs the YOLOv11 object detection architecture, Paddle OCR for optical character
recognition, and edge-compatible processing workflows for real-time performance.
The system's architecture design surrounds four major modules: real-time vehicle detection,
speed estimation through pixel displacement, license plate recognition, and alert
notification. Initial steps incorporate an Ultralytics version of the YOLOv11 model-an
effective, deep-learning-based convolutional neural network-to identify the presence of
vehicles in video streams, either live or recorded. With high confidence, the model
distinguishes between objects of interest such as cars, trucks, motorcycles, and buses in
each frame. It detects at a rate of 30 frames per second, ensuring applicability for
deployment in real-time applications.