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Implementation of YOLOv9 and V2X Technology for Traffic Signal Priority

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abidikshit/Yolov9-VancouverTrafficCongestion

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Transit Poverty: Leveraging AI to Transform Transportation

Click the video below to play a preview of our traffic detection system:

Watch the video

Description

Hey everyone! Exciting updates from our ongoing project tackling transit poverty through artificial intelligence. 🚗💻

We've implemented YOLO v9 (You Only Look Once) (released February 21st, 2024) to count incoming traffic for intersections in real-time. That's right; no more manual counting! 🙌

Currently, we're working on calculating vehicle speed and predicting approaching arrival times to optimize traffic signal timing. Think personalized green lights just for you! GREEN MEANS GO🚦

But wait, there's more! We're diving into reinforcement learning using V2X technology. What does that mean? Our system learns from the environment and makes decisions based on rewards – like giving priority to emergency vehicles or pedestrians. Mind blown yet? 🤯

And did we mention we're using YOLO object detection too? 🔍 It's like having our very own superhero watching over traffic flow. Safety and efficiency, anyone? 🛑️

The journey continues as we dive deeper into the realm of deep learning. Stay tuned for more exciting developments and join us in creating a smarter, safer future for all. 🌐✨

Contributors

Special thanks to our dedicated contributors who have made significant contributions to this project:

  • Murtaza Vora
  • Gunjan Paladiya
  • Milan Prajapati
  • Chinthaka Dinesh

How to run the script

Run the yolov9c_vehicle_count_tracker.ipynb script in Jupyter Lab to get started and feel free to make adjustments based on your need.

License

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