A lot of the groundwork's already been done from the OpenCV side.
OpenCV has a dnn[1] module that can import models from other frameworks like Caffe, TF and DarkNet (YOLO's native framework) into OpenCV's own DNN object model, and run inference on them. Both Yolo and R-CNN families have been implemented as examples[2].
What's missing is that OpenCV does not abstract all these behind a single object detection interface if that's what the LiveCV side expects. So such a plugin would have to define such an interface itself and provide an adapter per model.
Prototyping YOLO or the other DNN means just run detector and see the boxes. Of course training these systems is clearly out of reach for this kind of abstraction technology, not to mention modify and develop something new.
