title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÑÐµÐ¼ÐºÐ¸ Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½Ð¾Ð³Ð¾ Ð»ÐµÑÐ°ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Machine Vision Algorithms for Recognizing Aerial Objects of an Unmanned Aerial Vehicle
Creators: name: ÐÐ¸Ð»Ð¸ÑÑÐ½, ÐÐ¸ÐºÑÐ¾Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
deep neural networks
machine vision
deep learning
recognition of objects
intelligent systems
artificial intelligence

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¾ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ðµ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð´Ð°Ð½Ñ Ð¾Ð±ÑÐ¸Ðµ ÑÐµÑÐ¼Ð¸Ð½Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð², Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾ÑÐ¾ÑÑÑ Ð±ÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½Ð° ÑÐµÐ°Ð»ÑÐ½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐ»Ð° Ð¿Ð¾Ð´ÑÐ¾Ð±Ð½Ð¾ Ð¸Ð·ÑÑÐµÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² ÑÑÐµÐ´Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Matlab. ÐÑÐ¾Ð¼Ðµ ÑÑÐ¾Ð³Ð¾, Ð² ÑÐ¾Ð´Ðµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ñ Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÑÑÑ Ð½Ð°Ð¸Ð»ÑÑÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ð¸ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÐ¼ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð±ÑÐ»Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ Ð² ÑÐ¾Ð´Ðµ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½ÑÐ¼ Ð»ÐµÑÐ°ÑÐµÐ»ÑÐ½ÑÐ¼ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ¾Ð¼ Ð²Ð¾Ð´Ð½ÑÑ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐµÐ¹. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð¹ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÑÐ¸Ð²Ð° Ð´Ð°Ð½Ð½ÑÑ, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð±ÑÐ» Ð¿Ð¾Ð»ÑÑÐµÐ½ Ð² ÑÐ¾Ð´Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ÑÑÐ¾Ð´Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑ Ð¼ÐµÑÐ¾Ð´ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¾Ð±ÑÐ°Ð·Ð¾Ð² Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²Ð¾Ð¼ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ð¹. ÐÑÐ»Ð° Ð¾ÑÑÑÐµÑÑÐ²Ð»ÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð² ÑÑÐµÐ´Ðµ Matlab.
description: This paper presents a description of the construction of a model of an intelligent system based on deep learning. In the course of the work, general terms and classification of teaching methods for deep neural networks were given. Algorithms were also analyzed, on the basis of which training was conducted on real data. The training of deep convolutional neural networks in the Matlab development environment was studied in detail. In addition, in the course of this work, mathematical models of deep learning were identified that demonstrate the best results in the classification and recognition of the studied objects in the images that were obtained during monitoring by an unmanned aerial vehicle of water surfaces. A deep convolutional neural network was also trained on the basis of the data array that was obtained during the software processing of the original images using the method of image transformation by a variety of transformations. An artificial intelligence system program was developed in the Matlab environment.