  Drone detection has become an essential task in object detection as drone
costs have decreased and drone technology has improved. It is, however,
difficult to detect distant drones when there is weak contrast, long range, and
low visibility. In this work, we propose several sequence classification
architectures to reduce the detected false-positive ratio of drone tracks.
Moreover, we propose a new drone vs. bird sequence classification dataset to
train and evaluate the proposed architectures. 3D CNN, LSTM, and Transformer
based sequence classification architectures have been trained on the proposed
dataset to show the effectiveness of the proposed idea. As experiments show,
using sequence information, bird classification and overall F1 scores can be
increased by up to 73% and 35%, respectively. Among all sequence classification
models, R(2+1)D-based fully convolutional model yields the best transfer
learning and fine-tuning results.
