Traffic Management System

Project Overview

This project introduces an automated traffic management system built using advanced computer vision techniques. Designed to align with the smart city objectives of Saudi Vision 2030, the system leverages YOLOv8 to perform real-time vehicle detection and classification.

The Challenge

Urban traffic congestion requires dynamic solutions. Traditional static traffic lights often lead to inefficiencies and increased wait times. The goal was to develop an intelligent system capable of "seeing" current traffic conditions and adapting accordingly.

The Solution

By implementing a YOLOv8-based object detection model, the system analyzes live camera feeds to count vehicles across multiple lanes. This real-time data allows the system to accurately determine traffic density and dynamically adjust traffic light timings to optimize the flow of vehicles, reducing congestion and emissions.

Technologies Used

- YOLOv8: State-of-the-art, real-time object detection model.
- Python: Core programming language for processing feeds and model inferences.
- Computer Vision: Used to segment lanes, process frames, and track vehicles.