Fully supervised and point-supervised ship detection using center prediction, LUVSS-2021-11

In monitoring of maritime environment the detection of ships from aerial or satellite images is a common task. Although many fully supervised object detection methods can achieve excellent result on this domain, such methods remain limited by the amount of labeling required to create the training images. In this technical report, we explore novel methods for fully and weakly supervised learning of ship detector from satellite images. We propose a novel dense prediction method for object detection that can be used in fully supervised learning mode to achieve state-of-the-art results, while further modification allows for learning on weakly labeled data such as point-supervision. Point-supervision, where only as single point/pixel on object is known, can be applied to fully automated the learning of ship detection method by using openly available satellite images and known positions of ships from the database of global ship tracking AIS. This makes methods that can be trained from point-supervision highly suitable for ship detection domain.