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.

翻译：商用及国防领域对空中无人机的应用已在诸多方面受益，因而被广泛部署于多个不同的应用领域。然而，无人机也日益被用于针对性攻击，构成了重大的安全挑战，并使得无人机检测系统的开发成为必要。当前基于视觉的无人机检测系统存在精度限制，尤其在鸟类体型较小时，难以有效区分无人机与鸟类。本研究提出了一种新颖的YOLOBirDrone架构，旨在提升鸟类与无人机的检测与分类精度。YOLOBirDrone包含多个组件，包括自适应扩展层聚合模块（AELAN）、多尺度渐进式双重注意力模块（MPDA）以及用于保持形状信息、并通过局部与全局空间及通道信息增强特征的反向MPDA模块（RMPDA）。本文同时引入了一个大规模数据集BirDrone，其中包含用于鲁棒空中目标识别的小型及具有挑战性的目标。实验结果表明，相较于其他先进算法，所提出的YOLOBirDrone架构在各项性能指标上均有所提升，在不同场景下的检测精度可达约85%。