Multimodal Deep Learning Framework for Enhanced Accuracy of UAV Detection

• Published in: Computer Vision Systems Vol. 11754; pp. 768 - 777
• Main Authors: Diamantidou, Eleni, Lalas, Antonios, Votis, Konstantinos, Tzovaras, Dimitrios
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Data fusion; Deep learning; Detection; Drones; UAV
• ISBN: 3030349942; 9783030349943
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-34995-0_70

Counter-Unmanned Aerial Vehicle (c-UAV) systems are considered an emerging technology dedicated to address the critical issue of malicious UAV detection. Acquiring useful information from a multitude of data gathered using a topology of different sensors for UAV detection constitutes a problem with substantial importance. In this paper, we present a novel multimodal deep learning methodology to filter and combine data from a variety of unimodal approaches dedicated to UAV detection. Specifically, the aim of this work is to detect, and classify potential UAVs based on a fusion procedure of features from UAV detections provided by unimodal components. Actually, we propose a general fusion neural network framework in order to merge features extracted from unimodal modules and make deductions with increased accuracy. Our method is validated by thorough application to UAV detection and classification tasks. Our model approach achieves significant performance improvement over the unimodal detection results.