标题： RadioGalaxyNET：扩展射电星系及其红外宿主探测的数据集与新计算机视觉算法 显示英文标题

标题： RadioGalaxyNET: Dataset and Novel Computer Vision Algorithms for the Detection of Extended Radio Galaxies and Infrared Hosts

摘要： 从下一代深度巡天中创建射电星系目录需要自动识别扩展源的关联组件及其对应的红外宿主。 本文介绍了RadioGalaxyNET，这是一个多模态数据集以及一套新颖的计算机视觉算法，旨在自动化检测和定位多组件扩展射电星系及其对应的红外宿主。 该数据集包含来自2800张图像中的4155个星系实例，这些图像具有射电和红外通道。 每个实例提供关于扩展射电星系类别、包含所有组件的对应边界框、像素级分割掩码以及其对应红外宿主星系关键点位置的信息。 RadioGalaxyNET是首个包含澳大利亚平方公里阵列探路者（ASKAP）射电望远镜的高灵敏度图像、对应的红外图像以及星系检测实例级别注释的数据集。 我们在该数据集上评估了几种目标检测算法，并提出了一种新颖的多模态方法，以同时检测射电星系和红外宿主的位置。 显示英文摘要

摘要： 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.