АВТОМАТИЗАЦИЯ КОНТРОЛЯ КАЧЕСТВА НА ПРОИЗВОДСТВЕ С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ И КОМПЬЮТЕРНОГО ЗРЕНИЯ: РАЗРАБОТКА И АНАЛИЗ ЭФФЕКТИВНОСТИ СИСТЕМЫ

Abstract

This article discusses methods for automating the quality control process in industrial enterprises using machine learning and computer vision. Special attention is paid to convolutional neural networks and their use for detecting defects in images. Specific examples of quality control system implementation in Python are given, describing the main steps in developing and using convolutional neural networks, as well as methods for evaluating system effectiveness. Overall, automating the quality control process using machine learning and computer vision is an effective way to improve product quality and optimize production processes.