Yes, that is kind of a creative notation that I’ve never seen in exactly that form before. I think that is just expressing how “non-max suppression” works here. It excludes the values for the objects that are being dropped because they fall below the IOU threshold. Prof Ng covered that concept in the lectures and it’s also covered in the YOLO assignment, although he does not give the full details on how the loss function works.
I think the point is that is what the 1_k^{truth} value takes care of in an analogous way to how the IOU value worked above: it’s a Boolean vector that will be zero for the k values for which there are no recognized objects.
But I have never really looked at the YOLO loss function logic before, so I’m just winging it here. There are lots of great threads on the forum about YOLO. E.g. here’s one with some discussion of the loss function and there are more like this one that explain how YOLO works and is trained.