  Studying the universe through radio telescope observation is crucial.
However, radio telescopes capture not only signals from the universe but also
various interfering signals, known as Radio Frequency Interference (RFI). The
presence of RFI can significantly impact data analysis. Ensuring the accuracy,
reliability, and scientific integrity of research findings by detecting and
mitigating or eliminating RFI in observational data, presents a persistent
challenge in radio astronomy. In this study, we proposed a novel deep learning
model called EMSCA-UNet for RFI detection. The model employs multi-scale
convolutional operations to extract RFI features of various scale sizes.
Additionally, an attention mechanism is utilized to assign different weights to
the extracted RFI feature maps, enabling the model to focus on vital features
for RFI detection. We evaluated the performance of the model using real data
observed from the 40-meter radio telescope at Yunnan Observatory. Furthermore,
we compared our results to other models, including U-Net, RFI-Net, and R-Net,
using four commonly employed evaluation metrics: precision, recall, F1 score,
and IoU. The results demonstrate that our model outperforms the other models on
all evaluation metrics, achieving an average improvement of approximately 5\%
compared to U-Net. Our model not only enhances the accuracy and
comprehensiveness of RFI detection but also provides more detailed edge
detection while minimizing the loss of useful signals.
