| Summary: | The effectiveness of the YOLOv4-CSP convolutional neural network model in solving the problem of detecting objects moving in airspace is investigated. Images of flying objects of two classes were used as initial data for training and researching the convolutional neural network model: helicopter-type and aircraft-type unmanned aerial vehicles. Images of such objects were obtained in the optical and infrared wavelength ranges. Two datasets were formed from appropriately labeled source images with objects of these two classes. The first dataset was created from optical images, and the second from images obtained in the infrared wavelength range. The YOLOv4-CSP model was trained using training and validation samples from each dataset. Comprehensive studies of the effectiveness of the trained model were carried out using test samples from datasets. It is shown that the accuracy of detecting flying objects in optical images is higher than in images obtained in the infrared range, and the results for the speed of model calculation when analyzing optical and infrared images are close. Recommendations are given for the use of the YOLOv4-CSP model in computer vision systems for airspace monitoring.
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