title: ÐÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ Ð² Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾Ð¹ Ð´ÐµÑÐµÐºÑÐ¾ÑÐºÐ¾Ð¿Ð¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Computer vision in industrial flaw detection
Creators: name: ÐÐ¸Ð·Ð°Ð¼ÑÑÐ´Ð¸Ð½Ð¾Ð²Ð°, ÐÐ¸Ð»Ñ
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: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ð¹ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ
ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
neural network
x-ray control
technical vision
neural network training

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ½ÑÐ³ÐµÐ½Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð½ÐµÑÐ°Ð·ÑÑÑÐ°ÑÑÐµÐ³Ð¾ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ Ð´Ð»Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ ÑÐ²Ð°ÑÐ½ÑÑ ÑÐ²Ð¾Ð² ÑÑÑÐ±. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: Ð¾Ð±Ð·Ð¾Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸; ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ Ð¸ Ð°Ð½Ð½Ð¾ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹; ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð¿Ð¾ Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¸Ð¼ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÐ¼; Ð¾ÑÐµÐ½ÐºÐ° Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸; Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ñ Ð¸ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐ¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹. ÐÐ±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð¾ÑÑ Ð¿Ð¾ 53 Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÐ¼ Ñ Ð±ÑÐ°ÐºÐ¾Ð¼ Ð¸ 47 Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ð±ÐµÐ· Ð±ÑÐ°ÐºÐ° Ð½Ð° 25 ÑÐ¿Ð¾ÑÐ°Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð­ÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð°Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° 80%. Ð Ð°Ð±Ð¾ÑÐ° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python, Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ YOLO Ð¸ Ð¾ÑÐºÑÑÑÐ¾Ð³Ð¾ Python-ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐº Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ultralytics.
description: This work is devoted to the development and application of machine learning methods for detecting and classifying defects based on optical images. The paper discusses the use of the X-ray non-destructive testing method for diagnosing the condition of pipe welds. Problems that were solved during the study: review of artificial neural network technologies for recognition and classification; image creation and annotation; creation of an artificial neural network for detecting defects from optical images; assessing model performance; prospects and directions for further research. The neural network was trained using 53 images with defects and 47 images without defects over 25 training epochs. The experimental accuracy of the model was 80%. The work was performed using the Python programming language, the YOLO neural network and the open Python machine learning framework Ultralytics.