The results of experiments with a prototype of an autonomous infrared system for recognition of ground objects based on domestic physical components and open architecture of the YOLOv3 convolutional neural network are presented. The object of recognition is a car van. The neural network is trained on a set of images taken in the visible range. Infrared video footage of imperfect quality recorded by a moving and vibrating air carrier – octocopter – is analysed.

The efficiency of using Zernike moments when working with digital images obtained in the infrared region of the spectrum is considered to improve the accuracy and speed of an autonomous thermal imaging system. The theoretical justification of the choice of Zernike moments for solving computer (machine) vision problems and the choice of a suitable threshold binarization method is given. In order to verify the adequacy and expediency of using the chosen method, practical studies were conducted on the use of Zernike methods for distorting various thermal images in shades of gray.

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