Abstract: In Radio astronomy,RFI refers to all phenomena that interfere with the weak astronomical signals received by radio telescopes and have a significant impact on the observation and study of astronomical signals.The existing suppression methods at present,such as manual labeling methods,signal processing methods,machine learning and deep learning methods.This paper proposes an improved deep learning neural network model based on DeepLabV3+,aiming to detect RFI in astronomical data.It trains the model using the real RFI observation data observed by the Yunnan Observatory and fully utilizes the ability of the convolutional neural network to extract image features,so that the model can better learn the features of RFI and thereby achieve more accurate RFI detection.It adopts the model to recognize the image.By estimating the probability that each data point belongs to RFI and using the trained model to determine whether RFI exists or not.When a certain data point is predicted as RFI by the model,we mark it as the interfering part;otherwise,we mark it as the non-interfering part,thereby achieving the marking and suppression of the part with RFI in the image.The experimental results show that the proposed method demonstrates a satisfactory level in terms of F1 score,Accuracy and MIoU.Meanwhile,the proposed model is compared with the traditional deep lear-ning model,and ablation experiments are conducted to further verify its performance advantages.

Key words: RFI, Deep learning, Image processing, Radio astronomy, Interference suppression