With some changes - yes. I did this in my experimental project: https://github.com/indutny/resistenz/blob/master/python/mode...

The idea is to add an extra 2 params to the output of each classifier cell. Then do L2 normalization on them ( https://github.com/indutny/resistenz/blob/master/python/mode... ) and treat them as a cosine/sine pair.

The loss in this case would be the Euclidean distance between the actual and predicted pairs, which is equal to "2 * (1 - cos(x-y))".

Darknet is a framework for Neural Networks, YOLO is more an algorithm focused on object detection I think it could be relatively easy to perform the detection of an object's detection.

Does your training dataset supply that info about each object?

Capsule networks would be better suited for that