Drone-vs-Bird Detection Challenge at IEEE AVSS2019

Coluccia, Angelo; Saqib, Muhammad; Sharma, Nabin; Blumenstein, Michael; Magoulianitis, Vasileios; Ataloglou, Dimitrios; Dimou, Anastasios; Zarpalas, Dimitrios; Daras, Petros; Craye, Céline; Ardjoune, Salem; Fascista, Alessio; Iglesia, David de la; Olivares-Méndez, Miguel A.; Dosil, Raquel; Gonzalez, Izamar; Schumann, Arne; Sommer, Lars; Ghenescu, Marian; Piatrik, Tomas; Cubber, Geert De; Nalamati, Mrunalini; Kapoor, Ankit

doi:10.1109/avss.2019.8909876

Cited by 66 publications

(43 citation statements)

References 14 publications

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“…The threats and the system requirements considered in this project has been discussed by the authors in [35]. The project SafeShore has also launched a specific challenge, namely the drone-vs-bird detection challenge, dedicated to addressing one of the many technical issues arising in this context [36], [37]. Another solution is the one that is being developed within another EU project, namely Advanced hoListic Adverse Drone Detection, Identification & Neutralization (ALADDIN).…”

Section: Related Workmentioning

confidence: 99%

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

et al. 2020

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We propose Picking a Needle in a Haystack (PiNcH ), a methodology to detect the presence of a drone, its current status, and its movements by leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH is fully passive and it requires just cheap and general-purpose hardware. To evaluate the effectiveness of our solution, we collected real communication traces originated by a drone running the widespread ArduCopter open-source firmware, currently mounted on-board of a wide range (30+) of commercial amateur drones. Then, we tested our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movements of the drone; and, finally, (iv) enjoy a reduced upper bound on the time required to identify a drone with the requested level of assurance. The effectiveness of PiNcH has been also evaluated in the presence of both heavy packet loss and evasion attacks. In this latter case, the adversary modifies on purpose the profile of the traffic of the drone-RC link to avoid the detection. In both the cited cases, PiNcH continues enjoying a remarkable performance. Further, the comparison against state of the art solution confirms the superior performance of PiNcH in several scenarios. Note that all the drone-controller generated data traces have been released as open-source, to allow replicability and foster follow-up. Finally, the quality and viability of our solution, do prove that network traffic analysis can be successfully adopted for drone identification and status discrimination, and pave the way for future research in the area.

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Section: Related Workmentioning

confidence: 99%

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

et al. 2020

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“…The given examples of drone incidents show the need to monitor the flight of drones. To guarantee security, some drone producer companies have set up no-fly zones by prohibiting drones from flying within a 25 km radius of a few sensitive zones, such as airports, prisons, power plants, and other critical facilities [ 5 ]. However, the impact of no-fly zones is exceptionally constrained, and not all drones have those built-in safeguards.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, drones can reach speeds of up to 100 miles per hour, which imposes additional requirements on the speed of detection. To address these problems, the Drone-vs-Bird detection challenge [ 5 ] was established. The challenge provides video sequences in which drones are present along with birds.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time and Accurate Drone Detection in a Video with a Static Background

Seidaliyeva

Akhmetov

Ilipbayeva

et al. 2020

Sensors

130

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With the increasing number of drones, the danger of their illegal use has become relevant. This has necessitated the creation of automatic drone protection systems. One of the important tasks solved by these systems is the reliable detection of drones near guarded objects. This problem can be solved using various methods. From the point of view of the price–quality ratio, the use of video cameras for a drone detection is of great interest. However, drone detection using visual information is hampered by the large similarity of drones to other objects, such as birds or airplanes. In addition, drones can reach very high speeds, so detection should be done in real time. This paper addresses the problem of real-time drone detection with high accuracy. We divided the drone detection task into two separate tasks: the detection of moving objects and the classification of the detected object into drone, bird, and background. The moving object detection is based on background subtraction, while classification is performed using a convolutional neural network (CNN). The experimental results showed that the proposed approach can achieve an accuracy comparable to existing approaches at high processing speed. We also concluded that the main limitation of our detector is the dependence of its performance on the presence of a moving background.

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“…To address this problem, several solutions were proposed in 2017 in ShafeShore "bird-vs-drone challenge" to distinguish UAVs from birds in video records. Most approaches using deep learning for object detection are based on the assumption that UAVs and birds have different shapes [1]. However, this assumption is not always true.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning based Framework for UAV Trajectory Pattern Recognition

Pan

Desbarats

Chaumette

2019

2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Recognizing the trajectory patterns of Unmanned Aerial Vehicles (UAV) allows to identify their missions also to detect abnormalities for intelligent supervision. To take advantage of the developments of deep learning on image classification, we proposed a method that converts 3D trajectory data into 2D images and trains a deep network adapted to sketch-like images. We achieved a promising recognition rate of 99.5% on the database of simulated UAV trajectory images.

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Drone-vs-Bird Detection Challenge at IEEE AVSS2019

Cited by 66 publications

References 14 publications

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

Real-Time and Accurate Drone Detection in a Video with a Static Background

A Deep Learning based Framework for UAV Trajectory Pattern Recognition

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