Sequence models for drone versus bird classification

Fatih C. Akyon; Erdem Akagündüz; Sinan O. Altinuc; Alptekin Temizel

doi:10.1117/12.3023457

3 April 2024 Sequence models for drone versus bird classification

Fatih C. Akyon, Erdem Akagündüz, Sinan O. Altinuc, Alptekin Temizel

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 1307207 (2024) https://doi.org/10.1117/12.3023457
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

Abstract

Drone detection has become an essential task in object detection as drone costs have decreased and drone technology has improved. It is, however, difficult to detect distant drones when there is weak contrast, long range, and low visibility. In this work, we propose several sequence classification architectures to reduce the detected false-positive ratio of drone tracks. Moreover, we propose a new drone vs. bird sequence classification dataset to train and evaluate the proposed architectures. 3D CNN, LSTM, and Transformer based sequence classification architectures have been trained on the proposed dataset to show the effectiveness of the proposed idea. As experiments show, using sequence information, bird classification and overall F1 scores can be increased by up to 73% and 35%, respectively. Among all sequence classification models, R(2+1)D-based fully convolutional model yields the best transfer learning and fine-tuning results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fatih C. Akyon, Erdem Akagündüz, Sinan O. Altinuc, and Alptekin Temizel "Sequence models for drone versus bird classification", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 1307207 (3 April 2024); https://doi.org/10.1117/12.3023457

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