RadioGalaxyNET: Dataset and Novel Computer Vision Algorithms for the
  Detection of Extended Radio Galaxies and Infrared Hosts
Nikhel Gupta Zeeshan Hayder Ray P. Norris Minh Huynh Lars Petersson
Creating radio galaxy catalogues from next-generation deep surveys requires
automated identification of associated components of extended sources and their
corresponding infrared hosts. In this paper, we introduce RadioGalaxyNET, a
multimodal dataset, and a suite of novel computer vision algorithms designed to
automate the detection and localization of multi-component extended radio
galaxies and their corresponding infrared hosts. The dataset comprises 4,155
instances of galaxies in 2,800 images with both radio and infrared channels.
Each instance provides information about the extended radio galaxy class, its
corresponding bounding box encompassing all components, the pixel-level
segmentation mask, and the keypoint position of its corresponding infrared host
galaxy. RadioGalaxyNET is the first dataset to include images from the highly
sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope,
corresponding infrared images, and instance-level annotations for galaxy
detection. We benchmark several object detection algorithms on the dataset and
propose a novel multimodal approach to simultaneously detect radio galaxies and
the positions of infrared hosts.