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Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge

: Coluccia, Angelo; Fascista, Alessio; Schumann, Arne; Sommer, Lars; Dimou, Anastasios T.; Zarpalas, Dimitrios; Mendez, Miguel; Iglesia, David de la; Gonzalez, Iago; Mercier, Jean Philippe; Gagne, Guillaume; Mitra, Arka; Rajashekar, Shobha

Fulltext urn:nbn:de:0011-n-6351060 (60 MByte PDF)
MD5 Fingerprint: 8736183a8c885b74c38824038b366df9
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Created on: 27.5.2021

Sensors. Online journal 21 (2021), No.8, Art. 2824, 27 pp.
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
Journal Article, Electronic Publication
Fraunhofer IOSB ()
drone detection; deep learning; drone vs. bird; automatic recognition; image and video signal processing

Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.