Abstract
The use of aerial drones for commercial and defense applications has benefited in many ways and is therefore utilized in several different application domains. However, they are also increasingly used for targeted attacks, posing a significant safety challenge and necessitating the development of drone detection systems. Vision-based drone detection systems currently have an accuracy limitation and struggle to distinguish between drones and birds, particularly when the birds are small in size. This research work proposes a novel YOLOBirDrone architecture that improves the detection and classification accuracy of birds and drones. YOLOBirDrone has different components, including an adaptive and extended layer aggregation (AELAN), a multi-scale progressive dual attention module (MPDA), and a reverse MPDA (RMPDA) to preserve shape information and enrich features with local and global spatial and channel information. A large-scale dataset, BirDrone, is also introduced in this article, which includes small and challenging objects for robust aerial object identification. Experimental results demonstrate an improvement in performance metrics through the proposed YOLOBirDrone architecture compared to other state-of-the-art algorithms, with detection accuracy reaching approximately 85% across various scenarios.
Abstract (translated)
用于商业和国防应用的无人机在许多方面带来了益处，因此被应用于多个不同的领域。然而，它们也越来越多地被用作有针对性的攻击手段，这提出了重大的安全挑战，并且促使开发了无人机检测系统。基于视觉的无人机检测系统目前存在准确性限制，并且难以区分鸟类与无人机，尤其是在鸟类体型较小的情况下。 这项研究工作提出了一种新的YOLOBirDrone架构，旨在提高对鸟类和无人机的检测及分类准确度。该架构包含多个组件，包括自适应扩展层聚合（AELAN）、多尺度渐进双注意力模块（MPDA）以及逆向MPDA（RMPDA），后者用于保留形状信息并利用局部和全局空间及通道信息来丰富特征。 文章还引入了一个大规模的数据集BirDrone，其中包含小型且具挑战性的物体，有助于实现稳健的空中目标识别。实验结果显示，与现有的先进算法相比，通过提出的YOLOBirDrone架构，在性能指标上有所提升，检测准确率在各种场景下达到了约85%。
URL
https://arxiv.org/abs/2601.08319
