AI Traffic Signal Optimizer Using YOLOv8 – Django Final Year Project

About the Project

The AI Traffic Signal Optimizer is a full-stack Django final year project that brings real computer vision into everyday traffic management. Instead of fixed-timer signals, this system watches each lane at a junction through YOLOv8 object detection, works out how congested every direction actually is, and recalculates green-light durations on the fly. It is built for students who want a project that goes beyond a basic CRUD app and shows genuine applied AI skills to evaluators and interview panels alike.

Project Features

• Real-time vehicle detection and counting per lane using YOLOv8 and OpenCV
• Adaptive signal timing algorithm that scales green time with traffic density, bounded by configurable min/max limits
• Live NOC-style dashboard built with Tailwind CSS and Chart.js, auto-refreshing through JS polling
• Dedicated live detection view for uploading footage or images per lane
• Analytics dashboard tracking historical signal logs and density trends
• REST API layer built on Django REST Framework for all detection and signal data
• Management commands to seed demo junctions and simulate traffic cycles without needing camera footage
• Configurable density thresholds and timing rules in a single settings file

Applications

This concept applies directly to smart city traffic control, campus and township junction management, toll plaza queue balancing, and any research or prototype work around adaptive infrastructure. It is also a strong base for extending into multi-junction coordination or emergency-vehicle priority systems.

Who It's For

BTech CSE, BCA, MCA, and BSc IT students looking for a final year or major project in AI, computer vision, or Django, especially those who want a demo-able system with a working detection pipeline rather than a static report-only submission. It also suits students preparing for placement interviews who want to speak confidently about a real YOLOv8 integration.

Why Choose This Project

Unlike many pre-built Django final year projects that only demonstrate database operations, this one runs an actual object detection model end to end, ties detection output into a working algorithm, and visualizes results live. You get the full source code, setup guidance, and a project report structured for college submission. For students exploring other AI final year projects with source code, check out our AI/ML project category and related computer vision builds like our PlantPulse AI and BradykinesiaCam listings.