A threshold that high will be useless as it will miss most errors. A threshold low enough to catch most errors will reject some valid data. A naive approach lik

A threshold that high will be useless as it will miss most errors. A threshold low enough to catch most errors will reject some valid data. A naive approach like that will not work. A better approach would be to include temporal data in the inputs to the neural net so it can learn how to do the prediction and filtering itself using all the context available in the input imagery, instead of processing each frame completely independently and feeding low-dimensional symbolic results into some other system. But you'd need a very large dataset and a very large neural net.