  This paper presents a generalized model for real-time detection of flying
objects that can be used for transfer learning and further research, as well as
a refined model that achieves state-of-the-art results for flying object
detection. We achieve this by training our first (generalized) model on a data
set containing 40 different classes of flying objects, forcing the model to
extract abstract feature representations. We then perform transfer learning
with these learned parameters on a data set more representative of real world
environments (i.e. higher frequency of occlusion, very small spatial sizes,
rotations, etc.) to generate our refined model. Object detection of flying
objects remains challenging due to large variances of object spatial
sizes/aspect ratios, rate of speed, occlusion, and clustered backgrounds. To
address some of the presented challenges while simultaneously maximizing
performance, we utilize the current state-of-the-art single-shot detector,
YOLOv8, in an attempt to find the best trade-off between inference speed and
mean average precision (mAP). While YOLOv8 is being regarded as the new
state-of-the-art, an official paper has not been released as of yet. Thus, we
provide an in-depth explanation of the new architecture and functionality that
YOLOv8 has adapted. Our final generalized model achieves a mAP50 of 79.2%,
mAP50-95 of 68.5%, and an average inference speed of 50 frames per second (fps)
on 1080p videos. Our final refined model maintains this inference speed and
achieves an improved mAP50 of 99.1% and mAP50-95 of 83.5%
