An Image-based Classification Module for Building a Data Fusion Anti-drone System

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.