title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð¸ÑÑÐ°Ð½ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÐ»ÑÑÐºÐ¾Ñ Ð¾Ð·ÑÐ¹ÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ° Ð² Ð·Ð°Ð´Ð°Ð½Ð½Ð¾Ð¼ ÑÐµÐ³Ð¸Ð¾Ð½Ðµ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
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 an algorithm for remote optical localization of agricultural transport in a given region based on neural networks
Creators: name: ÐÐ¾ÐµÐ²Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
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: 2024
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐµ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
machine learning
computer vision
object recognition in video streaming
convolutional neural networks

description: ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÑÑÑÑ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð² Ð·Ð°Ð´Ð°ÑÐµ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ° Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐµ. Ð¦ÐµÐ»ÑÑ Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÐ½Ð½Ð¾Ð³Ð¾ ÐºÐ»Ð°ÑÑÐ° Ð¾Ð±ÑÐµÐºÑÐ° Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐµ. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ð¸Ñ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ, Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ Ð²ÑÐ±ÑÐ°Ð½Ð° ÑÐ°Ð¼Ð°Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÐ»ÑÑÐºÐ¾ÑÐ¾Ð·ÑÐ¹ÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ° Ð² Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÐµ. ÐÐ¾Ð´Ð³Ð¾ÑÐ¾Ð²Ð»ÐµÐ½Ñ Ð¾Ð±ÑÑÐ°ÑÑÐ¸Ð¹ Ð¸ ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¹ ÑÐµÑÑÐ¾Ð²ÑÐ¹ Ð´Ð°ÑÐ°ÑÐµÑ, Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾ÑÐ¾ÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð° Ð¸ Ð¿ÑÐ¾Ð²ÐµÑÐµÐ½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ YOLOv8. Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð° Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ° Ð² Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð¾Ñ ÑÑÐµÐ¿ÐµÐ½Ð¸ ÑÐºÑÐ°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð¾ÑÐ²ÐµÑÐµÐ½Ð¸Ñ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð¼ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° ÑÐ·ÑÐºÐµ PYTHON Ð² ÑÑÐµÐ´Ðµ Google Colab, Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÑÐ°Ð¹Ð¼Ð²Ð¾ÑÐºÐ° Keras Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² YOLOv8. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¼Ð¾Ð¶ÐµÑ ÑÐ²Ð»ÑÑÑÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ¾Ð¼ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð½ÑÐ¼ Ð² ÑÐµÐ»ÑÑÐºÐ¾ÑÐ¾Ð·ÑÐ¹ÑÑÐ²ÐµÐ½Ð½Ð¾Ð¼ Ð´ÑÐ¾Ð½Ðµ.
description: The subject of research of the graduate qualification work is convolutional neural networks in the task of recognition and localization of an object in a video stream. The purpose of the graduate qualification work is to develop an algorithm for recognizing and localizing a certain class of object in the video stream. In this paper we consider the models of convolutional neural networks and their comparison, based on the results of which the most appropriate model is selected to solve the problem of optical localization of agricultural transport in the video stream. Training and synthetic test dataset were prepared, on the basis of which the YOLOv8 model was trained and tested. The dependence of object recognition accuracy depending on the degree of shielding and illumination is considered. The result is an algorithm implemented in the PYTHON language in the Google Colab environment using the Keras framework and the YOLOv8 object recognition model. The developed algorithm can be a tool for solving problems of automatic localization installed in an agricultural drone.