title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð² ÐºÐ¾Ð½ÑÐµÐºÑÑÐµ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ 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°
titleType: Subtitle
title: Research and development of alghoritms for data augmentation in the context of image processing my machine learning
Creators: name: Ð¡Ð¼Ð¸ÑÐ½ÑÑ, Ð Ð¾Ð´Ð¸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: 2024
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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ÐÐ°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ
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description: ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð°ÑÑÐ¾ÑÑÐµÐ¹ 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¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹ (GAN) Ð¸ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Grad-CAM Ð² ÐºÐ¾Ð½ÑÐµÐºÑÑÐµ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð¦ÐµÐ»Ñ ÐÐÐ  â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð¾Ð±ÑÑÐ°ÑÑÐ¸Ñ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ, ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð² Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ. ÐÐµÑÐ¾Ð´ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÐÐÐ : ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð². ÐÐ°Ð´Ð°ÑÐ¸ ÐÐÐ  Ð²ÐºÐ»ÑÑÐ°ÑÑ: ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÑ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹ Ð½Ð° ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð°Ð½Ð½ÑÑ; Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Grad-CAM Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð² ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸ÑÑ; ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ ResNet, DenseNet Ð¸ EfficientNet Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ±Ð»Ð°ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÐÐÐ : Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð² Ð·Ð°Ð´Ð°ÑÐ°Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ; ÑÐ»ÑÑÑÐµÐ½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹; Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ²Ð¾Ð´Ñ: Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ GAN Ð¸ Grad-CAM ÑÐ»ÑÑÑÐ¸Ð»Ð¾ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹; ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð°Ñ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ° Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ ÑÐ»ÑÑÑÐ¸ÑÑ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð² Ð¸ Ð²ÑÑÐ²Ð»ÑÑÑ Ð´ÐµÑÐµÐºÑÑ Ð² Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ; Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð±Ð»Ð¾ÐºÐ¾Ð² Squeeze-and-Excitation Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÑ Ð² ÑÐµÐ°Ð»ÑÐ½ÑÑ Ð·Ð°Ð´Ð°ÑÐ°Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð¢Ð°ÐºÐ¸Ð¼ Ð¾Ð±ÑÐ°Ð·Ð¾Ð¼, Ð´Ð°Ð½Ð½Ð¾Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ¶Ð´Ð°ÐµÑ Ð°ÐºÑÑÐ°Ð»ÑÐ½Ð¾ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÐµÑÐµÐ´Ð¾Ð²ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¸Ñ Ð·Ð½Ð°ÑÐ¸Ð¼Ð¾ÑÑÑ Ð´Ð»Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¾Ð±Ð»Ð°ÑÑÑÑ.
description: The subject of this graduate qualification work is the development and application of data augmentation methods using generative adversarial networks (GAN) and Grad-CAM visualization methods in the context of image processing with machine learning methods. The objective of the work is the development of a generative adversarial network for augmenting training datasets, improving classification models, and detecting defects in images. Method of research: experiments and comparison of results. The tasks of the work include: developing and training generative adversarial network models on various datasets; applying Grad-CAM visualization methods to detect defects in classification visualizations; testing ResNet, DenseNet, and EfficientNet models using augmented data. The application area of the work results includes: automation of data augmentation processes in machine learning tasks; improving the quality of classification models; applying visualization methods for evaluating machine learning models. Conclusions: using GAN and Grad-CAM improved the efficiency of image processing; the developed methodology allows improving the performance of classifiers and detecting defects in images; modifying GAN models with Squeeze-and-Excitation blocks has shown effectiveness and applicability in real machine learning tasks. Thus, this study confirms the relevance of advanced data augmentation methods and their significance for improving machine learning models in various fields.