  This paper presents a new loss function for the prediction of oriented
bounding boxes, named head-tail-loss. The loss function consists in minimizing
the distance between the prediction and the annotation of two key points that
are representing the annotation of the object. The first point is the center
point and the second is the head of the object. However, for the second point,
the minimum distance between the prediction and either the head or tail of the
groundtruth is used. On this way, either prediction is valid (with the head
pointing to the tail or the tail pointing to the head). At the end the
importance is to detect the direction of the object but not its heading. The
new loss function has been evaluated on the DOTA and HRSC2016 datasets and has
shown potential for elongated objects such as ships and also for other types of
objects with different shapes.
