title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÑÑÐ¾Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¾ÑÑ Ð¾Ð´Ð¾Ð²
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: Development of a neural network for the recognition of construction waste
Creators: name: ÐÐ°Ð»ÑÐºÐ¾Ð², 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: 2024
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÐµ Ð¾ÑÑ Ð¾Ð´Ñ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÑ
Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ
Ð¿Ð¾Ð»ÑÐºÐ¾Ð½ÑÑÐ¾Ð»Ð¸ÑÑÐµÐ¼Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
deep learning
classification
detection
segmentation
computer vision
optimization
hyperparameters
dataset
semi-supervised learning

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÑÑÐ¾Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¾ÑÑÐ¾Ð´Ð¾Ð²Â». Ð¦ÐµÐ»ÑÑ 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° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð½Ð° ÑÐµÑÐµÐ½Ð¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÑÑÐ¾Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¾ÑÑÐ¾Ð´Ð¾Ð². Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð², ÑÐ¿Ð¾ÑÐ¾Ð±ÑÑÐ²ÑÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð², Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸. Ð¡Ð¾Ð·Ð´Ð°Ð½ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² ÑÑÑÐ¾Ð¸ÑÐµÐ»ÑÑÑÐ²Ð°. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐµÐ¼Ð°Ð½ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð¼ÐµÑÐ¾Ð´Ðµ Ð¿Ð¾Ð»ÑÐºÐ¾Ð½ÑÑÐ¾Ð»Ð¸ÑÑÐµÐ¼Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½ ÐºÐ°ÑÐµÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°, Ð¿Ð¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ ÐºÐ¾ÑÐ¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð»Ð° ÑÐ¾ÑÐ¾ÑÐ¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¼Ð°Ð»Ð¾Ð¼ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ðµ ÑÐ°Ð·Ð¼ÐµÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ.
description: The subject of the graduate qualification work is âDevelopment of a neural network for the recognition of construction wasteâ. The aim of the work is to develop a neural network algorithm aimed at solving the problem of recognizing construction waste. The paper analyzes modern neural network algorithms that contribute to solving the problem of object recognition and methods of their optimization. A data set has been created to solve the problem of semantic segmentation of construction sites. A semantic segmentation algorithm based on the semi-supervised model learning method has been developed. A qualitative and comparative analysis of the developed algorithm was carried out, according to the results of which the model demonstrated good learning quality indicators on a small amount of marked data.