AIRPLANES DETECTION IN AERIAL IMAGES USING YOLO NEURAL NETWORK
DOI:
https://doi.org/10.18372/2306-1472.76.13149Keywords:
Convolutional neural network, object detection, real-time processing, unmanned aerial vehicleAbstract
Purpose: The represented research results are aimed to benchmark performance of state-of-the-art methods of objects detection. There were tested two popular single-stage neural networks based on the “you only looks once” approach. Methods: convolutional neural network, logistic regression, probabilistic theory, stochastic gradient descent. Results: The considered artificial neural network architectures for objects detection has been trained and applied for the particular task of the airplanes detection in aerial images taken from unmanned aerial vehicles and satellites. Discussion: Presented results of experimental verification prove their high detection ability, location precision and real-time processing speed using modern graphics processing unit. The considered neural networks can be easily re-trained for detection of different classes of ground objects.
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