YOLOBirDrone: Dataset for Bird vs Drone Detection and Classification and a YOLO based enhanced learning architecture

TL;DR

This paper introduces YOLOBirDrone, a novel deep learning architecture and a large dataset for improved bird and drone detection and classification in aerial imagery, addressing current accuracy limitations.

Contribution

The paper presents a new YOLO-based architecture with advanced modules and a large dataset, enhancing detection accuracy for small and challenging aerial objects.

Findings

Detection accuracy improved to approximately 85%.

Proposed architecture outperforms existing algorithms.

Introduces BirDrone dataset with challenging small objects.

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.