Real-Time Flying Object Detection with YOLOv8
Dillon Reis Jordan Kupec Jacqueline Hong Ahmad Daoudi
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 is ready for implementation. 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, small spatial sizes, rotations, etc.) to generate our refined model.
Object detection of flying objects remains challenging due to large variance
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 tradeoff between inference
speed and mAP. While YOLOv8 is being regarded as the new state-of-the-art, an
official paper has not been provided. Thus, we provide an in-depth explanation
of the new architecture and functionality that YOLOv8 has adapted. Our final
generalized model achieves an mAP50-95 of 0.685 and average inference speed on
1080p videos of 50 fps. Our final refined model maintains this inference speed
and achieves an improved mAP50-95 of 0.835.