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

• URL: http://arxiv.org/abs/2312.00306v1
• Date: Fri, 1 Dec 2023 02:54:38 GMT
• Title: RadioGalaxyNET: Dataset and Novel Computer Vision Algorithms for the Detection of Extended Radio Galaxies and Infrared Hosts
• Authors: Nikhel Gupta, Zeeshan Hayder, Ray P. Norris, Minh Huynh, and Lars Petersson
• Abstract summary: RadioGalaxyNET is a dataset of 4,155 instances of galaxies in 2,800 images with both radio and infrared channels. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder radio telescope.
• Score: 12.997324012222695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond [0.0]
  Radio interferometric images of diffuse sources present a challenge for image segmentation tasks. We introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys.
  arXiv  Detail & Related papers  (2024-08-20T14:03:21Z)
• RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey [8.628944866074555]
  We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies. We use Gal-DINO computer-vision networks to predict categories of radio morphology and bounding boxes for radio sources. We cross-match the radio sources in the catalogue with the infrared and optical catalogues, finding infrared cross-matches for 73% and photometric redshifts for 36% of the radio galaxies.
  arXiv  Detail & Related papers  (2024-03-21T08:52:39Z)
• Classification of compact radio sources in the Galactic plane with supervised machine learning [0.0]
  We focus on the classification of compact radio sources in the Galactic plane using both radio and infrared images as inputs. To this aim, we produced a curated dataset of 20,000 images of compact sources of different astronomical classes. The implemented tools and trained models were publicly released, and made available to the radioastronomical community for future application.
  arXiv  Detail & Related papers  (2024-02-23T09:47:42Z)
• NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images [64.92809155168595]
  This paper introduces a Multi-category Object Counting task to estimate the numbers of different objects in an aerial image. Considering the absence of a dataset for this task, a large-scale dataset is collected, consisting of 3,416 scenes with a resolution of 1024 $times$ 1024 pixels. The paper presents a multi-spectrum, multi-category object counting framework, which employs a dual-attention module to fuse the features of RGB and NIR.
  arXiv  Detail & Related papers  (2024-01-19T07:12:36Z)
• A Multimodal Dataset and Benchmark for Radio Galaxy and Infrared Host Detection [13.944126480815553]
  The dataset comprises 4,155 instances of galaxies in 2,800 images with both radio and infrared modalities. Our dataset is the first publicly accessible dataset that includes images from a highly sensitive radio telescope, infrared satellite, and instance-level annotations for their identification.
  arXiv  Detail & Related papers  (2023-12-11T08:19:38Z)
• Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels [9.857561410876682]
  We show that a weakly-supervised deep learning algorithm can achieve high accuracy in predicting pixel-level information. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs) We show that the model achieves a mAP$_50$ of 67.5% and 76.8% for radio masks and infrared host positions.
  arXiv  Detail & Related papers  (2023-08-09T18:10:05Z)
• Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling [65.17711407805756]
  Sionna is a GPU-accelerated open-source library for link-level simulations based on. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation.
  arXiv  Detail & Related papers  (2023-03-20T13:40:11Z)
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
  arXiv  Detail & Related papers  (2022-03-30T11:44:56Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting [73.9222625243696]
  Fingerprint-based localization methods propose a solution to this problem, but rely on a radio map that is effort-intensive to acquire. We automate the radio map acquisition phase using a software-defined radio (SDR) and a wheeled robot. We describe first localization experiments on this radio map using a convolutional neural network to regress for location coordinates.
  arXiv  Detail & Related papers  (2021-04-16T08:31:46Z)
• Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning [59.19469551774703]
  Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. We construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439 RGB-Infrared image pairs, covering urban roads, residential areas, parking lots, and other scenarios from day to night.
  arXiv  Detail & Related papers  (2020-03-05T05:29:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.