Drone-vs-Bird detection challenge at IEEE AVSS2017

Coluccia, Angelo; Ghenescu, Marian; Piatrik, Tomas; Cubber, Geert De; Schumann, Arne; Sommer, Lars; Klatte, Johannes; Schuchert, Tobias; Beyerer, Jürgen; Farhadi, Mohammad; Amandi, Ruhallah; Aker, Cemal; Kalkan, Sinan; Saqib, Muhammad; Sharma, Nabin; Daud, Sultan; Makkah, Khan; Blumenstein, Michael

doi:10.1109/avss.2017.8078464

Cited by 36 publications

(23 citation statements)

References 3 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 green, red, and blue bounding boxes correspond to detections by our lightweight YOLO detector, cascaded Haar detector and background subtraction algorithm, respectively. [43] One can see that, lightweight YOLO architecture has detected 3 aerial objects one drone and two birds, while producing no other false alarms. Considering the complexity of background, this is a remarkable performance.…”

Section: Simultaneous Detections With the Same Detector On A Single Fmentioning

confidence: 95%

“…For instance, even very small camera vibrations or sudden illumination perturbations can cause the drastically . The footage is provided by [43] scheme is the resulting confidence score, which can be used as a metric in the system. The operation of multiple overlaid images detection with a single detector is shown in Fig.…”

Section: Simultaneous Detections With the Same Detector On A Single Fmentioning

confidence: 99%

Deep learning-based strategies for the detection and tracking of drones using several cameras

Unlu

Zenou

Rivière

et al. 2019

IPSJ T Comput Vis Appl

119

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Commercial Unmanned aerial vehicle (UAV) industry, which is publicly known as drone, has seen a tremendous increase in last few years, making these devices highly accessible to public. This phenomenon has immediately raised security concerns due to fact that these devices can intentionally or unintentionally cause serious hazards. In order to protect critical locations, the academia and industry have proposed several solutions in recent years. Computer vision is extensively used to detect drones autonomously compared to other proposed solutions such as RADAR, acoustics and RF signal analysis thanks to its robustness. Among these computer vision-based approaches, we see the preference of deep learning algorithms thanks to their effectiveness. In this paper, we are presenting an autonomous drone detection and tracking system which uses a static wide-angle camera and a lower-angle camera mounted on a rotating turret. In order to use memory and time efficiently, we propose a combined multi-frame deep learning detection technique, where the frame coming from the zoomed camera on the turret is overlaid on the wide-angle static camera's frame. With this approach, we are able to build an efficient pipeline where the initial detection of small sized aerial intruders on the main image plane and their detection on the zoomed image plane is performed simultaneously, minimizing the cost of resource exhaustive detection algorithm. In addition to this, we present the integral system including tracking algorithms, deep learning classification architectures and the protocols.

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“…This can run on low-power application Snapdragon processor with efficient performance capabilities. In [7], authors release an interesting challenge dataset for bird vs drone detection in order to prevent smuggling using drones in shore areas. The idea is to generate an alert in case of presence of drones in videos where there might be birds as well flying in the air.…”

Section: Aerial Imagery Object Detectionmentioning

confidence: 99%

Aerial Multi-Object Tracking by Detection Using Deep Association Networks

Jadhav

Mukherjee

Kaushik

et al. 2020

2020 National Conference on Communications (NCC)

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A lot a research is focused on object detection and it has achieved significant advances with deep learning techniques in recent years. Inspite of the existing research, these algorithms are not usually optimal for dealing with sequences or images captured by drone-based platforms, due to various challenges such as view point change, scales, density of object distribution and occlusion. In this paper, we develop a model for detection of objects in drone images using the VisDrone2019 DET dataset. Using the RetinaNet model as our base, we modify the anchor scales to better handle the detection of dense distribution and small size of the objects. We explicitly model the channel interdependencies by using Squeeze-and-Excitation (SE) blocks that adaptively recalibrates channel-wise feature responses. This helps to bring significant improvements in performance at a slight additional computational cost. Using this architecture for object detection, we build a custom DeepSORT network for object detection on the VisDrone2019 MOT dataset by training a custom Deep Association network for the algorithm.

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Drone-vs-Bird detection challenge at IEEE AVSS2017

Cited by 36 publications

References 3 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

Deep learning-based strategies for the detection and tracking of drones using several cameras

Aerial Multi-Object Tracking by Detection Using Deep Association Networks

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