AI 中文总结
该研究针对未来射电望远镜海量数据的分析需求，提出YOLO-Chars两阶段深度学习框架，在SKA SDC1数据集上实现射电星系的自动探测与关键物理参数表征，性能具有竞争力，具备可扩展应用潜力。
AI 中文摘要
未来射电望远镜将产生越来越难以用传统统计方法分析的数据量，这推动了机器学习技术的应用。本研究提出YOLO-Chars（基于YOLO的射电源探测与表征框架），这是一个用于在巡天图像中自动探测和表征射电星系的两阶段深度学习框架。该框架使用平方公里阵列科学数据挑战1（SKA SDC1）数据集开发并评估。在第一阶段，定制的基于YOLO的多尺度检测模型用于在大天图中定位致密和延展源；在第二阶段，专用的源表征网络估算检测到源的物理属性，重点关注三个关键参数：流量密度、角大小和位置角。结果显示，YOLO-Chars在SKA SDC1基准上实现了具有竞争力的探测和表征性能，证明其作为下一代射电连续谱巡天可扩展框架的潜力。
英文摘要
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.
CommentsAccepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS). DOI: 10.1093/mnras/stag1553
