I’m curious to know if teams had success with ML in 2022. I know a lot of teams looked to do ball tracking with machine learning in 2022, but I wasn’t able to find many posts/threads of teams touting huge success with this sort of thing.
I built a ML model for the yellow balls in 2020 - it was fun and a neat little proof of concept, but it was pretty expensive to label + train (I outsourced the labeling - so expensive to label, resource/time intensive to train). Once I was done I realized I’d solidly over-engineered a pretty simple problem - finding a bright yellow object on grey carpet.
The 2022 balls were obviously different - the red/blue balls presented challenges with not matching other red/blue elements (bumpers). A lot of basic solutions of doing CV proved to be pretty mediocre (Pixy, RPi + PhotonVision, Limelight, etc.). A lot of this seemed due to a mix of calibration and differing lighting conditions, nuances with different cameras, the speed of available coprocessors, etc.
I started wondering if ML would have made for a more robust solution - but I’m not necessarily convinced. I imagined going to an event with an ML ball tracking solution and having an issue with tracking balls on a field due to different lighting conditions - which was an issue for a lot of teams this year. Changing the camera input and running the model against some images to get a level of confidence might be the best tuning route - but it seems VERY time consuming, compared to changing sliders in PhotonVision or Limelight and seeing the results in realtime. The idea of retraining the model at the event with new labeled data from video taken during a practice match or something seems unlikely (although obviously - that’s the last lever you’d pull).
I’m also not sure that running a ML model is computationally lighter than PhotonVision, so I’d still have concerns about running on something like a RPi and getting the performance we’d want. (Edit: This post talks about running an ML model on a RPi with/without a Coral - maxing out at 30fps with a Coral, which is better than the 3-5fps without, but still not great for a moving robot)
And finally - at the end of the day, it’s still just trying to solve the problem of finding a colored circle on grey carpet. I’m not sure ML would do this THAT much better than the existing CV algorithms available to teams in order to justify the cost of getting it all up and running.
I’d love to hear counter takes to this, success stories, or teams using ML for something besides ball tracking in 2022. ML is super cool - I wish I could teach students about how it works and get them interested in this sort of thing. It’s much more inspirational than changing sliders in a UI someone else built. The cost just seems very high, and the effectiveness does not seem better than simpler approaches.