title: ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¿Ð¾Ð´Ð±Ð¾ÑÐ° Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ²ÐµÑÐ´ÑÑ Ð±ÑÑÐ¾Ð²ÑÑ Ð¾ÑÑ Ð¾Ð´Ð¾Ð² Ð² ÑÐ¾ÑÑÐ°Ð²Ðµ ÑÐ¾Ð±Ð¾ÑÐ¾ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐ° ÑÐ¾ÑÑÐ¸ÑÐ¾Ð²ÐºÐ¸
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: Automation of the selection of a convolutional neural network architecture for the classification of solid household waste as part of a robotic sorting complex
Creators: name: ÐÑÑÐ¸Ð¿Ð¾Ð², ÐÐ½Ð´ÑÐµÐ¹
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: 2023
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ²ÐµÑÐ´ÑÑ Ð±ÑÑÐ¾Ð²ÑÑ Ð¾ÑÑ Ð¾Ð´Ð¾Ð²
Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð¿Ð¾Ð´Ð±Ð¾Ñ Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÑ
neural networks
deep learning
solid waste classification
automated architecture search

description: ÐÐ±ÑÐµÐºÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´Ð±Ð¾ÑÐ° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐ²ÐµÑÐ´ÑÑ Ð±ÑÑÐ¾Ð²ÑÑ Ð¾ÑÑÐ¾Ð´Ð¾Ð². Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´Ð±Ð¾ÑÐ° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ. Ð¡ÑÐµÐ´Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð²ÑÐ±ÑÐ°Ð½ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ð¹ Ð¿Ð¾Ð´ Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐµÐ½Ð¸Ñ, ÑÑÐ¾ÑÐ¼ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ Ð² ÑÐ°Ð±Ð¾ÑÐµ. ÐÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ Ð¾Ð±Ð·Ð¾ÑÐ° Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶Ð¸Ð»Ð¸ÑÑ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¸, ÑÑÐ»Ð¾Ð¶Ð½ÑÑÑÐ¸Ðµ ÐµÐ³Ð¾ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÑ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð°. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð½Ð¾Ð²Ð°Ñ ÑÑÐ½ÐºÑÐ¸Ñ ÑÐµÐ³ÑÐ»ÑÑÐ¸Ð·Ð°ÑÐ¸Ð¸, Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´Ð±Ð¾ÑÐ° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¸ Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð² ÑÐ°Ð¼Ð¾Ð¹ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐµ Ñ ÑÑÐµÑÐ¾Ð¼ Ð¿Ð¾ÑÐ»ÐµÐ´Ð½Ð¸Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ¾Ðº Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ²ÐµÑÐ´ÑÑ Ð±ÑÑÐ¾Ð²ÑÑ Ð¾ÑÑÐ¾Ð´Ð¾Ð² ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ñ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ñ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼ÑÑ ÑÐ»ÑÑÑÐµÐ½Ð¸Ð¹. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ñ, Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ¶Ð´Ð°ÑÑÐ¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð°. ÐÐ¾Ð»ÑÑÐµÐ½Ð½Ð°Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¾Ð±Ð»Ð°Ð´Ð°ÐµÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ 84 % Ð¸ Ð´Ð»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ ÑÑÐµÐ±ÑÐµÑ 757Â·106 Ð¾Ð¿ÐµÑÐ°ÑÐ¸Ð¹ Ñ Ð¿Ð»Ð°Ð²Ð°ÑÑÐµÐ¹ ÑÐ¾ÑÐºÐ¾Ð¹, ÑÑÐ¾ Ð² 7 ÑÐ°Ð· Ð¼ÐµÐ½ÑÑÐµ, ÑÐµÐ¼ Ñ Ð¸ÑÑÐ¾Ð´Ð½Ð¾Ð¹ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐ¹ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ Ð¾Ð±Ð»Ð°Ð´Ð°ÐµÑ Ð¿ÑÐ¸ÐµÐ¼Ð»ÐµÐ¼Ð¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ Ð¸ Ð½ÐµÐ±Ð¾Ð»ÑÑÐ¸Ð¼Ð¸ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐ¼Ð¸ Ð·Ð°ÑÑÐ°ÑÐ°Ð¼Ð¸, Ð¿Ð¾ÑÑÐ¾Ð¼Ñ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð² ÑÐ¾Ð±Ð¾ÑÐ¾ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¾Ð¹ ÑÑÑÐ°Ð½Ð¾Ð²ÐºÐµ.
description: The object of the research is the architectures of convolutional neural networks for image classification. The aim of the work is to research and develop a method of automated search of the neural network architecture for creating the architecture of the classifier of solid household waste. The paper presents an overview of methods of automated architecture search. Among the methods considered, the most suitable for the limitations formulated in the work is selected. According to the results of the review, the chosen method revealed shortcomings that complicate its practical applicability within the framework of the task. The paper suggests improvements to the chosen method. A new regularization function, an algorithm for automated architecture selection and changes in the architecture itself, taking into account the latest developments in the field of deep learning, are proposed. A dataset has been formed for the classification of solid household waste, as well as descriptions of the practical implementation of the proposed improvements. The paper presents experimental data confirming the effectiveness of the proposed method. The resulting convolutional neural network architecture has an accuracy ofÂ  Â  84 % and 757Â·106 floating-point operations are required to process one image, which is 7 times less than that of the original architecture. The resulting neural network classifier has acceptable accuracy and low computational costs, so it can be practically implemented in a robotic system.