title: ÐÐ½Ð°Ð»Ð¸Ð· Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²ÐµÐ½Ð½ÑÑ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
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° Ð¿Ð¾ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ 09.04.01 - ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐ° Ð¸ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐµÑ Ð½Ð¸ÐºÐ° ; 09.04.01_17 - ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ (Ð¼ÐµÐ¶Ð´ÑÐ½Ð°ÑÐ¾Ð´Ð½Ð°Ñ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð°Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°)
titleType: Subtitle
Creators: name: ÐÐ°Ð¸Ð½-Ð£Ð»Ñ-ÐÐ°Ð±Ð¸Ð´Ð¸Ð½, ---
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: 2019
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ðµ Ð·Ð½Ð°Ð½Ð¸Ð¹
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description: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð½ÐµÐ¸ÑÐ¿ÑÐ°Ð²Ð½Ð¾ÑÑÐµÐ¹ Ð² Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ðµ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² Ð°Ð½Ð°Ð»Ð¸Ð·Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð±ÑÐ»Ð¾ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¾ Ð² ÑÑÐ¾Ð¹ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ. ÐÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð»Ð¸ÑÐµÑÐ°ÑÑÑÐ½ÑÐ¹ Ð¾Ð±Ð·Ð¾Ñ ÑÑÐ¾Ð³Ð¾ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ°, Ð¸ Ð±ÑÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ñ Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¸Ð¼ÐµÑÑÐ¸ÐµÑÑ Ð² Ð»Ð¸ÑÐµÑÐ°ÑÑÑÐµ. ÐÐ¾ÑÐ»Ðµ Ð¾Ð±Ð·Ð¾ÑÐ° Ð»Ð¸ÑÐµÑÐ°ÑÑÑÑ Ð¼ÐµÑÐ¾Ð´ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ Ð´Ð»Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð±ÑÐ» Ð²ÑÐ±ÑÐ°Ð½ Ð´Ð»Ñ ÑÐ¾Ð¿ÑÑÑÑÐ²ÑÑÑÐµÐ¹ Ð¿ÑÐ¾ÐµÐºÑÐ½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ. ÐÐ»Ñ ÑÑÐ¾Ð¹ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¾ÑÐ¾Ð±ÑÐ°Ð½Ñ ÑÐµÑÑÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ SqueezeNet, GoogleNet, ResNet- 18 Ð¸ MobilenetV2, Ð¸ Ð¸Ñ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ Ð±ÑÐ»Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð° Ð¸ Ð¸Ð·Ð¼ÐµÑÐµÐ½Ð°. ÐÑÐ»Ð¾ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¾, ÑÑÐ¾ Ð´Ð»Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐµÑÑ Googlenet ÑÐ²Ð»ÑÐµÑÑÑ Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½Ð¾Ð¹, ÑÐµÐ¼ Resnet-18, Ð° Squeezenet Ð¿Ð¾Ð´ Ð½Ð¾Ð¼ÐµÑÐ¾Ð¼ ÑÑÐ¸ MobilenetV2 ÑÐ²Ð»ÑÐµÑÑÑ Ð½Ð°Ð¸Ð¼ÐµÐ½ÐµÐµ ÑÐ¾ÑÐ½Ð¾Ð¹ Ð¸Ð· ÑÐµÑÑÑÐµÑ ÑÐµÑÐµÐ¹. ÐÐ´Ð½Ð°ÐºÐ¾ squeezenet ÑÐ²Ð»ÑÐµÑÑÑ Ð»ÑÑÑÐ¸Ð¼ Ñ ÑÐ¾ÑÐºÐ¸ Ð·ÑÐµÐ½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹, Ð·Ð° ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ ÑÐ»ÐµÐ´ÑÑÑ Googlenet, Resnet-18 Ð¸ MobilenetV2.
description: The fault detection in Manufacturing using Machine learning methods in Image analysis has been carried out in this research work. A literature review of this subject has been performed and the Machine learning techniques available in the literature have been observed. After the literature review the Transfer learning method for the Convolutional Neural Networks has been selected for accompanied project work. Four neural networks SqueezeNet, GoogleNet, ResNet-18 and MobilenetV2 have been selected for the purpose of this research work and their performance has been investigated and measured. It has been found that for the selected datasets and training methods the Network Googlenet is more accurate than Resnet-18 and with Squeezenet at number three MobilenetV2 is the least accurate of the four networks. However squeezenet is best in terms of computation speed followed by Googlenet, Resnet-18 and MobilenetV2.