# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
###### Abstract  
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 using 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. drone detection; deep learning; drone vs. bird; automatic recognition; image and video signal processing +
Footnote †: journal: Sensors  
0000-0001-9001-9000][PERSON] 0000-0001-900 transport [5]. Since aircraft and birds share the same airspace at lower altitudes, the hazard of bird strikes always exists. The cost of bird strikes to the aviation industry is estimated to be more than one billion euros annually [6]. Similarly, unmanned aerial vehicles (UAVs) bring an important set of challenges to the aviation industry. Despite the restrictions of flying drones near airports, and reserved airspaces to ensure that they do not infringe each others' area, there are still several challenges involved. Adopting effective detection and countermeasure techniques to face the rising threat of small drones is a great concern of security agencies and governments today. There is indeed a gap in current surveillance systems, since the small size and fast maneuvers make drones a difficult category of targets to be detected, compared to traditional aircrafts [7; 8; 9; 10; 11]. Surveillance and detection technologies based on different modalities are under investigation, with different trade-offs in terms of complexity, maximum coverage in range, and capabilities [12]. Technologies that have been employed include 3D LIDAR sensors, passive radio detectors, video analytics, and passive acoustic sensors [13; 14]. All these technologies have pros and cons, if individually considered, and can be effective only under specific operating conditions. Systems implementing an integrated approach that combines the benefits of different technologies have been recently investigated also in Horizon 2020 European research projects SafeShore ( [[http://safeshore.eu](http://safeshore.eu)]([http://safeshore.eu](http://safeshore.eu)), accessed on: 14 April 2021) [15] and ALADDIN ( [[https://aladdin2020.eu](https://aladdin2020.eu)]([https://aladdin2020.eu](https://aladdin2020.eu)), accessed on: 14 April 2021). Technology can be well utilized in such cases to reduce human effort and provide analytical data and representation. Artificial Intelligence (AI) is one such field that tries to mimic how humans learn and is an active research field. This paper focuses on recent advances on the detection of drones or birds using machine learning based approaches, more specifically deep learning algorithms. The need for such sophisticated methods stems from the recognised difficulty for air traffic controllers to detect and differentiate between birds and drones.