There’s a pretty vast gulf between 70s era threshold, edge detect, morphology type methods and deep learning.

Robust feature detectors, voting methods, etc. can fill that gap for object detection and pose estimation. Also simpler machine learning models for classification like SVMs.

It increasingly feels like the slice of computer vision techniques I learned in the early 2000s is receding in relevance. But at the same time, there ought to be a more modern grab bag of robust tools to build engineered solutions.

In the end any algorithm is only as good as the dataset it’s developed/validated against. Ad-hoc methods may fail in bright sunshine, but so too would a neural network if that condition never arises in its training set.