Advanced Traffic Monitoring & Speed Regulation using YOLOVII
| dc.contributor.author | Alwin Jose-1NH22CE002, Prathyek K- 1NH22CE018 | |
| dc.date.accessioned | 2026-02-12T12:03:43Z | |
| dc.date.available | 2026-02-12T12:03:43Z | |
| dc.date.issued | 2025 | |
| dc.description.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. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/21014 | |
| dc.language.iso | en | |
| dc.publisher | New Horizon College of Engineering | |
| dc.title | Advanced Traffic Monitoring & Speed Regulation using YOLOVII | |
| dc.type | Learning Object |