TensorFlowObjectDetector (TFOD) for FTC

What is this?

This repository contains an Android library which enables FTC teams to use machine learning in their OpModes. Specifically, this library makes it possible to use neural networks to do object detection on camera frames. This library requires very little setup, and once running will update recognitions in the background without user interaction, enabling the user to focus on other tasks. Furthermore, this repository contains scripts to enable teams to collect and annotate their own datasets, should they wish to learn more or experiment with different models.

Why did you do this?

Perhaps a better question is what we're hoping to accomplish by releasing this library. In no particular order, here are some of our goals.

1. Democratize machine learning. We believe machine learning is becoming a fundamental technology that everyone (e.g. all FTC teams) should be able to benefit from and have access to, and this is a step in that direction.
2. Enable higher levels of autonomy. It's no secret that the reason for machine learning's success has been its unparalleled performance and accuracy across a wide range of real world tasks. By bringing some of this technology to FTC, we hope to push the limits of what autonomous robots can do.
3. Educate people about ML. We want students to learn about the whole process of using and developing machine learning models, to help them better understand the true extents of what is possible.

How do I get started?

Depending on when you are reading this, this library may or may not already be prepackaged with the robot controller app. If that is the case, then you can jump directly to the usage guidelines. If not, or if you're trying to use this library outisde of the FTC codebase (which is supported, acceptable, and encouraged!), you'll first want to follow the steps in the Installation section. You can then continue to the usage section, as above.

How does it work?

Definitions:

• frame: A single image, from e.g. a camera or a video
• model: A pretrained machine learned model which operates on frames
• recognition: Output from the model, annotating all objects in a frame
• tracker: An object tracker to smoothly interpolate recognitions

The library's operation can be thought of as being broken into a few distinct steps:

1. Initialize a source for frames
2. Load the labels and models into memmory
3. Start background processing and other objects

Then, the following runs indefinitely:

1. Get a new frame
2. If possible, pass the frame to the model
3. Feed the frame through the tracker
4. If the model is done processing, feed its results into the tracker
5. Make the most recent model results (recognitions) available to the client

A more detailed understanding of each of the steps involved can be found in the documentation and comments for each of the parts of the system.

Acknowledgements

We would like to thank Aakanksha Chowdhery, Vivek Rathod, and Ronny Votel for their help and support with TensorFlow Lite and the TensorFlow Object Detection API. We would also like to thank Vasu Agrawal for his tireless work in developing this library, David Konerding and Liz Looney for their mentorship throughout the development cycle, as well as the folks on the FTC SDK team (especially Tom Eng, Bob Atkinson, and Craig MacFarlane) for their help with FTC-speciic integration. Finally, we would like to thank everyone involved in coordinating and participating in our data collection event, including Patricia Cruz, Aaron Cunningham, Calvin Johnson, Nathan Mulcahey, and FTC teams 8381, 11039, 12454, 12635, 12869, and 13799.