  As drones become increasingly prevalent in human life, they also raises
security concerns such as unauthorized access and control, as well as
collisions and interference with manned aircraft. Therefore, ensuring the
ability to accurately detect and identify between different drones holds
significant implications for coverage extension. Assisted by machine learning,
radio frequency (RF) detection can recognize the type and flight mode of drones
based on the sampled drone signals. In this paper, we first utilize Short-Time
Fourier. Transform (STFT) to extract two-dimensional features from the raw
signals, which contain both time-domain and frequency-domain information. Then,
we employ a Convolutional Neural Network (CNN) built with ResNet structure to
achieve multi-class classifications. Our experimental results show that the
proposed ResNet-STFT can achieve higher accuracy and faster convergence on the
extended dataset. Additionally, it exhibits balanced performance compared to
other baselines on the raw dataset.
