Head-tail Loss: A simple function for Oriented Object Detection and Anchor-free models
This is an incremental improvement for oriented object detection in computer vision, particularly useful for detecting elongated objects.
The paper tackles the problem of oriented object detection by introducing a new loss function called head-tail loss, which minimizes distances between predicted and ground-truth key points, and it shows potential on DOTA and HRSC2016 datasets for elongated objects like ships.
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.