In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is to provide an overview of existing techniques, in terms of prediction performance and computational efficiency, to scientists in the astrophysics community who would like to employ machine learning in their research.

翻译：近年来，深度学习已成功应用于多个科学领域。受其显著成果与性能推动，该技术近期也开始在射电天文学领域得到评估。特别是随着全球最大望远镜——平方公里阵列（SKA）的诞生，射电天文学已进入大数据时代，自动目标检测与实例分割任务对于源搜寻与分析至关重要。本研究探索了最受认可的深度学习方法在射电干涉成像仪器获得的天文图像上的性能，以解决自动源检测问题。我们通过应用针对目标检测与语义分割这两类不同任务设计的模型展开研究，旨在为天体物理学界希望将机器学习应用于自身研究的科研人员，提供现有技术在预测性能与计算效率方面的综述。