Drone-vs-Bird Detection Challenge at ICIAP 2021

Coluccia, Angelo; Fascista, Alessio; Schumann, Arne; Sommer, Lars; Dimou, Anastasios; Zarpalas, Dimitrios; Sharma, Nabin; Nalamati, Mrunalini; Eryuksel, Ogulcan; Ozfuttu, Kamil Anil; Akyon, Fatih Çağatay; Sahin, Kadir; Buyukborekci, Efe; Cavusoglu, Devrim; Altinuc, Sinan Onur; Xing, Daitao; Unlu, Halil Utku; Evangeliou, Nikolaos; Tzes, Anthony; Nayak, Abhijeet; Bouazizi, Mondher; Ahmad, Tasweer; Gonçalves, Artur; Rigault, Bastien; Jain, Raghvendra; Matsuo, Yutaka; Prendinger, Helmut; Jajaga, Edmond; Rushiti, Veton; Ramadani, Blerant; Pavleski, Daniel

doi:10.1007/978-3-031-13324-4_35

Cited by 9 publications

(1 citation statement)

References 21 publications

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“…To recognize and classify UAVs, in our project work described in [28,29], we have investigated differentiating drones from birds in a YOLO v4 platform. In this paper, we propose to use the GoogleNet convolutional neural network in the Optical-Electronic Channel Data Fusion.…”

Section: The Study Materials and Methodsmentioning

confidence: 99%

Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

Semenyuk

Kurmashev

Lupidi³

et al. 2023

EEJET

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This work reports a study into the possibility of using the GoogleNet neural network in the optoelectronic channel of the Data Fusion system. The search for the most accurate algorithms for detecting and recognizing unmanned aerial vehicles (UAVs) in Data Fusion systems has been carried out. The data processing scheme was selected (merging SVF state vectors and merging MF measurements), as well as the sensors and recognition models on each channel of the system. The Data Fusion model based on the Kalman Filter was chosen, integrating radar and optoelectronic channels. Mini-radars LPI-FMCW were used as a radar channel. Evaluation of the effectiveness of the selected Data Fusion channel model in UAV detection is based on the recognition accuracy. The main study is aimed at determining the possibility of using the GoogleNet neural network in the optoelectronic channel for UAV recognition under conditions of different range classes. The neural network for the recognition of drones was developed using transfer training technology. For training, validation, and testing of the GoogleNet neural network, a database has been built, and a special application has been developed in the MATLAB environment. The capabilities of the developed neural network were studied for 5 variants of the distance to the object. The detection objects were the Inspire 2, DJI Phantom 4 Pro, DJI F450, DU 1911 UAVs, not included in the training database. The UAV recognition accuracy by the neural network was 98.13 % at a distance of up to 5 m, 94.65 % at a distance of up to 20 m, 92.47 % at a distance of up to 20 m, 90.28 % at a distance of up to 100 m, and 88.76 % at a distance of up to 200 m. The average speed of UAV recognition by this method was 0.81 s.

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Section: The Study Materials and Methodsmentioning

confidence: 99%