I have a problem that has bothered me quite some time.

With modern methods object detectors can often be accurately trained, even with small to medium sized datasets. However, there is one thing where I always see errors and that is when objects are very close together. The problem seems to be that the commonly used clustering methods like NMS will often combine predictions in a wrong way, like in this example:

So far, I found that one rather hacky way to solve this is to only annotate some part of the object as the parts won't be close together, but I wonder if there could be a nicer way to solve this. Many solutions like bottom-up keypoint detection look promising, but they seem to only show their strength with large training datasets - with less data I saw a lot of confusion between the objects.

My question - has anybody experienced something similar and maybe has a promising paper or implementation for a solution that could work without huge datasets?