I'm using an object detection neural network and I employ data augmentation to increase a little my small dataset. More specifically I do rotation, translation, mirroring and rescaling.
I notice that rotating an image (and thus it's bounding box) changes its shape. This implies an erroneous box for elongated boxes, for instance on the augmented image (right image below) the box is not tightly packed around the left player as it was on the original image.
The problem is that this kind of data augmentation seems (in theory) to hamper the network to gain precision on bounding boxes location as it loosens the frame.
Are there some studies dealing with the effect of data augmentation on the precision of detection networks? Are there systems that prevent this kind of thing?
Thank you in advance!
(Obviously, it seems advisable to use small rotation angles)