  One fundamental limitation to the research of bird strike prevention is the
lack of a large-scale dataset taken directly from real-world airports. Existing
relevant datasets are either small in size or not dedicated for this purpose.
To advance the research and practical solutions for bird strike prevention, in
this paper, we present a large-scale challenging dataset AirBirds that consists
of 118,312 time-series images, where a total of 409,967 bounding boxes of
flying birds are manually, carefully annotated. The average size of all
annotated instances is smaller than 10 pixels in 1920x1080 images. Images in
the dataset are captured over 4 seasons of a whole year by a network of cameras
deployed at a real-world airport, covering diverse bird species, lighting
conditions and 13 meteorological scenarios. To the best of our knowledge, it is
the first large-scale image dataset that directly collects flying birds in
real-world airports for bird strike prevention. This dataset is publicly
available at https://airbirdsdata.github.io/.
