https://github.com/shaunmulligan/radr
An AI powered rear facing radar for your bike. Instead of using mmWave radar tech, this project uses an inexpensive raspberry pi zero 2, a camera and Coral.ai TPU accelerator to detect objects behind you.
https://github.com/shaunmulligan/radr
ai coral-tpu cycling machine-learning python raspberry-pi
Last synced: 2 months ago
JSON representation
An AI powered rear facing radar for your bike. Instead of using mmWave radar tech, this project uses an inexpensive raspberry pi zero 2, a camera and Coral.ai TPU accelerator to detect objects behind you.
- Host: GitHub
- URL: https://github.com/shaunmulligan/radr
- Owner: shaunmulligan
- Created: 2023-07-03T16:04:33.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-04T17:07:33.000Z (over 2 years ago)
- Last Synced: 2024-04-17T09:53:52.800Z (over 1 year ago)
- Topics: ai, coral-tpu, cycling, machine-learning, python, raspberry-pi
- Language: Python
- Homepage:
- Size: 13.7 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Radr
An smart rear facing radar for your bike. Instead of using mmWave radar tech, this project uses an inexpensive raspberry pi zero 2, a camera and Coral.ai TPU accelerator to detect objects behind you.
## MVP features:
- Detect cars, cyclist (person + bicycle) and people behind you.
- Use the Coral.ai TPU accelerator to run the model, hopefully at ~ 25 FPS.
- Track multiple objects at once.
- Implement the radar BLE Gatt service to send alerts to a cycling computer.
- 3D print a case for the pi zero 2, camera, TPU accelerator and battery that slips in under the saddle.
## Future features:
- Add neopixel LEDs controllable via BLE Gatt service.
- brighten LEDs when an object is detected.
- Add ability to record video of the ride.
- periodically save images + detection while riding and upload to cloud
- Use the cloud to train a custom model on the images. Using something like v7Labs, roboflow or ClearML.
- Have Models be versioned and deployed to the device via wifi when device is charging.
## Usage
1. Build the container:
```bash
cd radr && sudo docker build -t radr-v1 .
```
2. Run the container:
```bash
sudo docker run --rm -it --privileged radr-v1
```
The `--privileged` flag is required to access the camera and USB accelerator.
3. You should now be able to see the video stream on `http://:8080/video`
## Testing with video file.
If a video file with `.mp4` extension is found in the root directory, the model will run on the video file instead of the camera. This offers a nice way to consistently test the model on a known input.
Example of the current model output:
