Real-time vehicle detection, traffic parameter analysis, and live UI visualization using YOLOv8 and Python.
Welcome to Traffic Flow Congestion—a computer vision-powered system that monitors traffic in real time, counts vehicles, and computes congestion metrics with a sleek, responsive UI. Built for urban insights, automation demos, and smart city prototypes.
- 🔍 Vehicle Detection: Uses Ultralytics YOLOv8 for high-speed, high-accuracy object detection.
- 📊 Traffic Metrics: Computes congestion parameters like vehicle count, density, and flow rate.
- 🖥️ Live Dashboard: Real-time UI built with PyQt5 to visualize traffic stats and detection overlays.
- ⚙️ Modular Design: Clean architecture with separate modules for detection, data processing, and UI rendering.
| Component | Technology Used |
|---|---|
| Detection Model | YOLOv8 (Ultralytics) |
| UI Framework | PyQt5 |
| Data Handling | pandas, JSON |
| Visualization | OpenCV, Matplotlib |
| Language | Python 3.x |
git clone https://github.com/devmdave/PythonTrafficCongestion.git
cd PythonTrafficCongestion