[Jared Russell]

Cool! This is AFAIK the first working NN implemented on an FRC robot.

Have you tried comparing your NN approach to a more traditional model-based vision approach?

With a single camera I'd suggest trying the Hough circle transform on an intensity or edge image. If you assume that balls are sitting on the ground plane and your camera is at a fixed height and angle, you can constrain the range of radii that you need to consider.

With a stereo/depth camera rig you can do even better; estimate and remove points near the floor plane, and then look only at the points that remain and cluster into spheres (using a 3D template matching algorithm or Hough sphere transform, only now scale is entirely fixed).

(C)NNs are awesome technology and are quickly become ubiquitous in computer vision (and if only for this reason alone, they are a worthwhile learning exercise for your team!) They excel at solving hard detection and classification problems where humans don't have good intuition about how to specify useful visual features and the relationships between them. But for tracking a roughly monochromatic sphere on a level surface, I'd wager that human intuition is pretty good.