Background. The relevant question of increasing the informative content and reliability of the thermal non-destructive testing is considered in this article. The most promising algorithms of digital processing of sequences of thermograms are given. Objective. The main aim of this research is to determine the advantages and disadvantages of the application of each considered method of digital processing of thermograms. Secondary, the possibilities of testing automation with the use of the selected methods of digital processing of thermograms are analyzed in this article. Methods. Computer simulation software was used to obtain the artificial sequence of the thermograms. Methods of wavelet analysis, principal components analysis and neural networks were used to process the received data. Results. The simulation of active thermal testing process is carried out in this research. The artificial thermogram sequence with a high level of noise is obtained for the object of testing. In order to quantify the results of application of considered methods, relative errors of determining the area of defects were calculated. Also values of Tanimoto criterion are obtained. The advantages of the neural network processing of digital data in thermal non-destructive testing have been established and proved in this article. Shape of defects on a binary map built by the neural network was closest to true compared with principal components analysis method. The effectiveness of neural networks is also confirmed by quantitative estimates. Conclusions. The method of wavelet transformation has a high sensitivity. This method is ineffective in the conditions of uneven heating and high noise. The principal components analysis method allows increasing the SNR and improving the visual perception of thermograms, but does not provide complete separation of information about defects and noises caused by uneven heating. Methods of artificial neural networks theory provide the best reproduction of the shape and size of the defects, but the training process requires significant time and computing resources.
Асиметричні криптографічні алгоритми конструюються на підґрунті розкладання достатньо великого натурального числа на прості множники, дискретного логарифмування на кінцевому полі з достатньо великою характеристикою, додавання точок з раціональними координатами ЕК на кінцевому полі. Алгоритми на ЕК потребують визначення коефіцієнтів самої кривої, здійснення операції на кінцевому полі характеристикою, бажано простим числом, знаходження базової точки раціональними координатами порядком простого числа, складних обчислень, пов'язаних із специфікою моделі алгоритму. Наведено умову вибору коефіцієнтів за знаком значення дискримінанта кубічного рівняння для забезпечення ефективності застосування алгоритмів на ЕК. З використанням формул Віета для коренів багаточленів наведено спосіб вибору коефіцієнтів. Зазначено інтервал вибору базової точки. Визначено формули дотичної до базової точки і пошуку координати точки перетину дотичної з ЕК. Отримана рекурентна формула додавання базової точки з іншими точками ЕК з раціональними координатами, яка є узагальненою формулою для додавання будь-яких точок ЕК з раціональними координатами. Ключові слова: еліптична крива, асиметричний криптоалгоритм, дискримінант, кубічне рівняння, формули Вієта, базова точка, порядок базової точки.
A method of automating the data analysis of thermal imaging systems in the field of safety control is proposed. It has been established that today video surveillance technologies have a number of disadvantages that can be eliminated by using thermal imaging cameras. Analysis of infrared images can be automated in order to reduce percentage of false positives and increase the efficiency of thermal imaging video surveillance systems. A high level of interference, unclear object contours and low image resolution are real problems in automating the object detecting process on thermographic images. The traditional and promising methods of thermograms analysis and approaches that can lead to creating the automated thermal video surveillance systems are discussed. It is proposed to use deep learning, which in recent years has proven itself as an effective way of image analysis. The study is based on review of existing works, as methods of automating object detection process on thermograms. It is proposed to use YOLOX as a deep learning model. This model has one of the best quality indicators and speed processing input parameters on standard datasets. FLIR’s Thermal Starter annotated set of thermal images is used to train the model. The value of mAP at the level 55% is obtained according the results of model training for recognizing 4 classes of objects on thermograms. Different advantages and disadvantages of this development are analyzed. Ways of further improvement of the neural network method of automation of thermal imaging safety control systems have been determined.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.