If you need an angled bounding box, you could probably modify any of the current approaches in that regard. You could also add a post-processing step where you take the predicted bounding boxes, rotate them in all possible directions, and predict the most likely one.
But if you're dealing with known geometric shapes, like e.g. rectangles, you'll get better results if you use "classic" detection algorithms that are already mathematically optimal.
For example, I once had to count the number of atoms in an electron-microscope image. I simply ran a circle detector with very sensitive settings, then culling overlapping circles with lower "circleness" score. That missed a few atoms that were stacked on top of others, but still got more accurate results than the previous method, which apparently involved a poor grad student ticking them off on paper.