Abstract
Means of air attack are pervasive in all modern armed conflict or terrorist action. We present the results of a NATO-SPS project that aims to fuse data from a network of optical sensors and low-probability-of-intercept mini radars. The requirements of the image-based module aim to differentiate between birds and drones, then between different kind of drones: copters, fixed wings, and finally the presence or not of payload. In this paper, we outline the experimental results of the deep learning model for differentiating drones from birds. Based on the trade-off between speed and accuracy, the YOLO v4 was chosen. A dataset refine process for YOLO-based approaches is proposed. The experimental results verify that such an approach provide a reliable source for situational awareness in a data fusion platform. However, the analysis indicates the necessity of enriching the dataset with more images with complex backgrounds as well as different target sizes.
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Notes
- 1.
https://antidrones-project.org/, last access 24.03.2022.
- 2.
https://github.com/chuanenlin/drone-net, last access 19.03.2022.
- 3.
https://github.com/tzutalin/labelImg, last access 19.03.2022.
- 4.
https://www.dvdvideosoft.com/products/dvd/Free-Video-to-JPG-Converter.htm, last access 19.03.2022.
- 5.
https://github.com/coderslagoon/BadPeggy, last access 19.03.2022.
- 6.
https://colab.research.google.com/, last access 20.03.2022.
- 7.
https://osf.io/jqmk2/, last access 18.03.2021.
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Aknowledgments
This work was funded by NATO SPS Programme, approved by Dr. A, Missiroli on 12 June, 2019, ESC(2019)0178, Grant Number SPS.MYP G5633. Total project Grant is Euro 398.000. Total project duration is 36 months, project kick-off date: 25 September 2019. NATO country Project Director Dr. A. Cantelli-Forti, co-director Dr. O. Petrovska, and Dr. I. Kurmashev. We thank Dr. Claudio Palestini, officer at NATO who oversees the project. We thank Prof. Biljana Stojkoska from the Ss. Cyril and Methodius University, for her scientific contribution and tutoring to the authors (Prof. Biljana is not a member of the project and had no access to the instrumental data). We thank author O. Petrovska for funding acquisition.
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Jajaga, E. et al. (2022). An Image-Based Classification Module for Data Fusion Anti-drone System. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13374. Springer, Cham. https://doi.org/10.1007/978-3-031-13324-4_36
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