I am struggling to find the time for this inquiry. Here is a question you may be able to answer for me: How often did you get false positives? False negatives?

I’ll eventually find the time to compile all the data of the vision programs in FRC the past few years: 341’s, 1706’s and yours, and do an analysis on each one. But that might be tricky considering I have zero of the materials they were all designed for.

Here is what @bernini (if we all start to do this, eventually chief delphi will add the feature, one can hope) was talking about with CNN (convolutional neural network) and SVM (support vector machine): http://yann.lecun.com/exdb/publis/pdf/huang-lecun-06.pdf

Your implementation of the same algorithm for FRC would yield better results due to the smaller scale of the network and SVM, I would suspect it to be 100 percent accurate in detecting with so few classes to classify something into (ball, robot, goal, etc…).

marshall: marshall: Thanks! Neither can we. We’ve got some plans we’re working on though. Something about depth perception and neural networks last I heard.

If you do go the neural network route, I highly suggest you have everyone involved in it watch Andrew Ng’s class on machine learning on coursera. I find it to be the best introduction to the topic. Fortunately, there is an amazing tool at your disposal for deep learning with (convolutional) neural networks: caffe. It is not as user friendly as it could be, but it is an extremely powerful tool. Something to keep you busy in the offseason (I would not leave the task of learning caffe plus getting a data set as well as designing a network during build season).