Abstract
The efficiency of the YOLOv4 convolutional neural network (CNN) in detection of objects moving in airspace is investigated. Video materials of two classes of flying objects (FO) were used as the initial data for training and testing of the CNN: helicopter-type unmanned aerial vehicles and gliders. Video materials were obtained in the optical and infrared (IR) wavelength ranges. Datasets are formed from them in the form of a set of images. Large-scale studies of the detection efficiency of the YOLOv4 CNN on such images have been conducted. It is shown that the accuracy of detection of FO in optical images is higher than in images obtained in the IR wavelength range.
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Tkachev, S., Markov, N. (2024). Detection of Flying Objects Using the YOLOv4 Convolutional Neural Network. In: Yavorskiy, R., Cavalli, A.R., Kalenkova, A. (eds) Tools and Methods of Program Analysis. TMPA 2021. Communications in Computer and Information Science, vol 1559. Springer, Cham. https://doi.org/10.1007/978-3-031-50423-5_16
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DOI: https://doi.org/10.1007/978-3-031-50423-5_16
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