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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a module for recognition of terrestrial spatial objects using an artificial neural network
Creators: name: Ð¨Ð¸Ð±Ð°Ð»Ð¾Ð², ÐÐ½ÑÐ¾Ð½
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: 2021
Subjects: ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð½Ð°Ð·ÐµÐ¼Ð½ÑÑ Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
recognition of ground three-dimensional objects
image segmentation
computer vision

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description: The topic of graduate qualifying work is Development of a module for recognition of terrestrial spatial objects based on an artificial neural network. This work is devoted to the creation of a program with a user interface, which makes it possible to recognize terrestrial spatial objects on a user-selected image from an unmanned aerial vehicle. When performing work for the neural network, on the basis of which the recognition module works, the structure, training and testing parameters were determined. The database for training the network was created and translated into the format required by the ANN for correct training. Testing of the artificial neural network was carried out using such evaluation criteria as the error matrix and ROC-curve. The program execution algorithm has been debugged, optimized, and a user interface has been implemented for the convenience of working with the program. As a result of the work, a program was obtained containing an ANN pre-trained on a set of images from an unmanned aerial vehicle, which recognizes objects in an image selected by the user.