Development of an image recognition algorithm for an automated system for monitoring fiberglass defects based on machine learning methods

Abstract

Development of an image recognition algorithm for an automated system for monitoring fiberglass defects based on machine learning methods

Kaznacheeva A.A., Vlasenko O.M., Zakharkina S.V., Goncharova E.B.

Incoming article date: 19.02.2025

The article proposes an image recognition algorithm for an automated fiberglass defect inspection system using machine learning methods. Various types of neural network architectures are considered, such as models with a firing rate of neurons, a Hopfield network, a restricted Boltzmann machine, and convolutional neural networks. A convolutional neural network of the ResNet model was chosen for developing the algorithm. To develop the algorithm, a convolutional neural network of the ResNet model was selected. As a result of testing the program, the neural network worked correctly with a high learning percentage.

Keywords: fiberglass, defects, machine learning, convolutional neural networks, ResNet architecture, testing, accuracy