解决无人机与鸟类混淆难题，提升空中目标识别准确率
YOLOBirDrone: Dataset for Bird vs Drone Detection and Classification and a YOLO based enhanced learning architecture
- 设计新型YOLO架构，融合多尺度注意力机制增强特征表达
- 在复杂场景下实现约85%检测准确率，优于现有方法
- 构建大规模真实数据集BirDrone，支持小目标精准识别
无人机在商业和国防领域应用广泛，但也日益被用于针对性攻击，带来安全威胁，亟需高效检测系统。当前基于视觉的无人机检测仍存在精度瓶颈，尤其难以区分小型无人机与鸟类。本文提出YOLOBirDrone架构，包含自适应扩展层聚合（AELAN）、多尺度渐进双注意力模块（MPDA）及反向MPDA（RMPDA），有效保留形状信息并融合局部与全局空间、通道特征。同时构建大规模数据集BirDrone，涵盖小而难辨的目标，支持鲁棒的空中物体识别。实验表明，该架构在多种场景下检测准确率达约85%，显著优于现有先进算法。
原文摘要 · Abstract (English)
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.
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