  Deep learning-assisted antenna design methods such as surrogate models have
gained significant popularity in recent years due to their potential to greatly
increase design efficiencies by replacing the time-consuming full-wave
electromagnetic (EM) simulations. However, a large number of training data with
sufficiently diverse and representative samples (antenna structure parameters,
scattering properties, etc.) is mandatory for these methods to ensure good
performance. Traditional antenna modeling methods relying on manual model
construction and modification are time-consuming and cannot meet the
requirement of efficient training data acquisition. In this study, we proposed
a deep learning-assisted and image-based intelligent modeling approach for
accelerating the data acquisition of antenna samples with different physical
structures. Specifically, our method only needs an image of the antenna
structure, usually available in scientific publications, as the input while the
corresponding modeling codes (VBA language) are generated automatically. The
proposed model mainly consists of two parts: Convolutional Neural Network (CNN)
and Long Short Term Memory (LSTM) networks. The former is used for capturing
features of antenna structure images and the latter is employed to generate the
modeling codes. Through training, the proposed model can achieve fast and
automatic data acquisition of antenna physical structures based on antenna
images. Experiment results show that the proposed method achieves a significant
speed enhancement than the manual modeling approach. This approach lays the
foundation for efficient data acquisition needed to build robust surrogate
models in the future.
