Classification of Low-Resolution Flying Objects in Videos Using the Machine Learning Approach (CROSBI ID 310256)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Stančić, Ivo ; Veić Lucija ; Musić, Josip ; Grujić, Tamara
engleski
Classification of Low-Resolution Flying Objects in Videos Using the Machine Learning Approach
A challenge of detecting and identifying drones has emerged due to the significant increase in recreational and commercial drones operating range, payload size, and overall capabilities. Consequently, drones may pose a risk to airspace safety or violate non-flying zone in the vicinity of vulnerable buildings. This paper presents an initial study for a machinelearning classification system applied to flying objects visible with a low resolution that is too distant from the camera to be efficiently classified by other methods. The original dataset in form of labeled high-resolution videos containing lowresolution drone, bird, and airplane objects was collected and carefully prepared. Computationally inexpensive features based on object shape and trajectory descriptors were recommended and tested with several ML models. The accuracy of the best- proposed model tested on our dataset was 98%. The results of this study demonstrate that Machine Learning classification seems to be promising and can be implemented in future multi-stage drone detection and identification system.
artificial neural networks, computer vision, feature extraction, machine learning, object detection.
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Podaci o izdanju
22 (2)
2022.
45-52
objavljeno
1582-7445
1844-7600
10.4316/AECE.2022.02006
Trošak objave rada u otvorenom pristupu
Povezanost rada
Elektrotehnika, Računarstvo, Vojno-obrambene i sigurnosno-obavještajne znanosti i umijeće