Radio Galaxies detection and characterization using deep learning techniques
TL;DR
This work presents YOLO-Chars (YOLO-based Detection and Characterisation of Radio Sources), a two-stage deep-learning framework for the automated detection and characterisation of radio galaxies in survey images using the Square Kilometre Array Science Data Challenge 1 dataset.
Abstract
Future radio telescopes will generate data volumes that are increasingly difficult to analyse using traditional statistical methods, motivating the adoption of machine-learning techniques. In this work, we present YOLO-Chars (YOLO-based Detection and Characterisation of Radio Sources), a two-stage deep-learning framework for the automated detection and characterisation of radio galaxies in survey images. The framework is developed and evaluated using the Square Kilometre Array Science Data Challenge 1 (SKA SDC1) dataset. In the first stage, customised YOLO-based multi-scale detection models are used to localise compact and extended sources across large sky maps. In the second stage, a dedicated source-characterisation network estimates the physical properties of the detected sources. We focus on three key parameters: flux density, angular size, and position angle. Our results show that YOLO-Chars achieves competitive detection and characterisation performance on the SKA SDC1 benchmark, demonstrating its potential as a scalable framework for next-generation radio continuum surveys.