An Image-based Classification Module for Building a Data Fusion Anti-drone System
Date Issued
2022
Author(s)
Jajaga, Edmond
Rushiti, Veton
Ramadani, Blerant
Pavleski, Daniel
Cantelli-Forti, Alessandro
Petrovska, Olivera
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.
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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