title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÐºÐ¾Ð½ÑÑÑÑÐºÑÐ¸Ð²Ð½ÑÑ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð² Ð½Ð° ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ð¼ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¸-ÑÐ½Ð¸Ð¼ÐºÐµ ÐÐ-ÐºÐ°Ð¼ÐµÑÑ
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: Recognition of structural elements in the technical image-image of an IR camera
Creators: name: Ð¡ÐµÑÐ³ÐµÐµÐ², ÐÐµÐ¾ÑÐ³Ð¸Ð¹
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: 2020
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ° Ð½Ð° ÑÐ½Ð¸Ð¼ÐºÐµ
Ð¸Ð½ÑÑÐ°ÐºÑÐ°ÑÐ½ÑÐ¹ ÑÐ¿ÐµÐºÑÑ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ñomputer vision
snapshot recognition
infrared spectrum
machine learning

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description: This work is devoted to an algorithm for detecting structural elements of a printed circuit board by its IR image. During the study, the following problems were solved: 1. Preparation of an information database of printed circuit boards IR snapshots 2. Research of the methods for detecting structural elements in the image 3. Construction of the algorithms for detecting and classifying elements in an infrared image 4. Analysis of the applicability of the algorithm according to the results of its work The paper presents a method for translating an IR image from a file of the "BMT" format into a grayscale image and into a pseudo-color image with subsequent improvement of its visual characteristics. For the image format "grayscale" an algorithm for detecting structural elements of high and low temperature is presented. The results of the algorithm are intermediate and are used in the classification algorithm. Two different classifiers based on neural networks are investigated in the work: a linear classifier with training by the SGD method and a classifier based on a convolutional neural network. Based on the results of the classifier, an image with selected structural elements is built and presented for the user. The detection and classification algorithm was tested on a test data set and the accuracy and limitations of the applicability of the algorithm were determined from the test results