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.

本文提出了一种广义飞行物实时检测模型，可用于迁移学习和进一步研究，同时提出了一种可用于实现的改进模型。通过将第一通用模型训练在包含 40 种不同类别的飞行物数据集上，我们可以使模型提取抽象特征表示，然后在代表真实世界环境的数据集上执行传递学习，从而生成改进的模型，并且我们尝试使用当前最先进的单发探测器 YOLOv8 来解决一些出现的挑战。最后得到的模型达到了 0.685 的 mAP50-95 和 50fps 的平均推断速度。

YOLOv8 实时飞行物体检测

Real-Time Flying Object Detection with YOLOv8

We propose an approach to detect flying objects such as UAVs and aircrafts
when they occupy a small portion of the field of view, possibly moving against
complex backgrounds, and are filmed by a camera that itself moves.
Solving such a difficult problem requires combining both appearance and
motion cues. To this end we propose a regression-based approach to motion
stabilization of local image patches that allows us to achieve effective
classification on spatio-temporal image cubes and outperform state-of-the-art
techniques.
As the problem is relatively new, we collected two challenging datasets for
UAVs and Aircrafts, which can be used as benchmarks for flying objects
detection and vision-guided collision avoidance.

提出了一种基于回归的运动稳定方法，旨在检测被拍摄的机动飞行器在复杂背景中移动的情况，该方法在运动与外观两方面共同作用下可以对比先进技术更有效地运用时空图像块进行分类，并通过收集两个具有挑战性的数据集为飞行物体检测和视觉引导式避碰创建基准。