i’d say, forget about cascades at all, having several of them is not really feasible, as there is no real way to get a match confidence, and computation will take ages (as you’ll probably have more than 3)
yea, that’s the real interesting part here 
take a lot of images, vary lighting a lot, and pose a little, i guess
things to try now:
- maybe template matching already works. just make sure NOT to use any _NORMED method there, so you can compare the probabilities
- split the detection from the classification, e.g. find contours, if they coarsely match your expectations, crop the region and throw it at a multiclass classifier (e.g. an SVM)
- what everyone else does: deep learning. (re-)train a yolo5 (pytorch) dnn on colab, using roboflow for annotations / preprocessing