scholarly journals Automatic condition monitoring method to find defects in high-voltage insulators using infrared images

2019 ◽  
Vol 124 ◽  
pp. 03003 ◽  
Author(s):  
A. D. Zaripova ◽  
D. K. Zaripov ◽  
A. E. Usachev
Keyword(s):  
Computer Vision ◽  
Condition Monitoring ◽  
High Voltage ◽  
Visual Analysis ◽  
Infrared Imaging ◽  
Imaging System ◽  
Electrical Equipment ◽  
Infrared Images ◽  
Monitoring Method ◽  
Thermal Imaging System

In recent years, infrared imaging has become an important tool, particularly for predicting and preventing electrical equipment failure. Systems for online monitoring of the equipment conditions used in electrical substations are based on computer vision algorithms to perform visual analysis, automatically detect and assess equipment condition. This article describes a developed method that automatically finds defects in high-voltage insulators using infrared images. This method is based on the Otsu method, which is one of the most popular and effective segmentation methods that can be applied to finding defects in infrared images. The result is a comparative analysis of computer vision methods in infrared images used in our research. Automatic condition monitoring to find defects in high-voltage insulators in infrared images can be considered as the base method for an automated thermal imaging system for monitoring electrical substation equipment.

scholarly journals Application of Zernike moments in computer vision problems for infrared images

2021 ◽  
Vol 310 ◽  
pp. 01002
Author(s):  
Dmitriy Otkupman ◽  
Sergey Bezdidko ◽  
Victoria Ostashenkova
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Thermal Imaging ◽  
Imaging System ◽  
Infrared Region ◽  
Zernike Moments ◽  
Infrared Images ◽  
Theoretical Justification ◽  
Thermal Imaging System ◽  
Binarization Method

The efficiency of using Zernike moments when working with digital images obtained in the infrared region of the spectrum is considered to improve the accuracy and speed of an autonomous thermal imaging system. The theoretical justification of the choice of Zernike moments for solving computer (machine) vision problems and the choice of a suitable threshold binarization method is given. In order to verify the adequacy and expediency of using the chosen method, practical studies were conducted on the use of Zernike methods for distorting various thermal images in shades of gray.

Ammonia and Near-Infrared Observations of Southern Dark Clouds

1993 ◽  
Vol 10 (3) ◽  
pp. 236-240 ◽  
Author(s):  
T.L. Bourke ◽  
A.R. Hyland ◽  
G. Robinson ◽  
S.D. James
Keyword(s):  
Near Infrared ◽  
Infrared Imaging ◽  
Imaging System ◽  
Ammonia Emission ◽  
Infrared Images ◽  
Infrared Source ◽  
Visual Appearance ◽  
Infrared Observations ◽  
Dark Clouds ◽  
Ammonia Source

AbstractThe Parkes radio telescope has been used to search a list of small, dense southern dark clouds and Bok globules for ammonia emission at 23.7 GHz. The ammonia observations, together with IRAS data and the cloud’s visual appearance, have been used to determine a short list of dark clouds for observation with the infrared imaging system (IRIS) on the Anglo-Australian Telescope, in an attempt to determine the dust density distribution within the clouds. Near-infrared images of a number of the short listed clouds have been obtained with IRIS at J, H and K’. Preliminary results are reported for this ammonia survey, together with IRIS images of the strong ammonia source DC 297.7–2.8. Coincident with the dense ammonia core of this object is an IRAS ‘core’ source, IRAS 11590–6452 and an extremely interesting near-infrared source, which lies on the edge of the error ellipse of the IRAS source.

scholarly journals Nonuniformity Correction of Single Infrared Images Based on Deep Filter Neural Network

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 612 ◽  
Author(s):  
Xianzhong Jian ◽  
Chen Lv ◽  
Ruzhi Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infrared Imaging ◽  
Imaging System ◽  
Infrared Images ◽  
Nonuniformity Correction ◽  
Training Images ◽  
Quantitative Measurements ◽  
Complex Training ◽  
Simulated Training

The fixed-pattern noise (FPN) caused by nonuniform optoelectronic response limits the sensitivity of an infrared imaging system and severely reduces the image quality. Therefore, nonuniform correction of infrared images is very important. In this paper, we propose a deep filter neural network to solve the problems of network underfitting and complex training with convolutional neural network (CNN) applications in nonuniform correction. Our work is mainly based on the idea of deep learning, where the nonuniform image noise features are fully learned from a large number of simulated training images. The network is designed by introducing the filter and the subtraction structure. The background interference of the image is removed by the filter, so the learning model is gathered in the nonuniform noise. The subtraction structure is used to further reduce the input-to-output mapping range, which effectively simplifies the training process. The results from the test on infrared images shows that our algorithm is superior to the state-of-the-art algorithm in visual effects and quantitative measurements, providing a new method for deep learning in nonuniformity correction of single images.

Thermo-visual Evaluation of the Yin-Tang Acupuncture Point for Intracranial Hypertension Syndrome

2003 ◽  
Vol 31 (03) ◽  
pp. 455-466 ◽  
Author(s):  
Aleck Ovechkin ◽  
Kyeong-Seop Kim ◽  
Jeong-Whan Lee ◽  
Sang-Min Lee
Keyword(s):  
Temperature Distribution ◽  
Intracranial Hypertension ◽  
Visual Analysis ◽  
Imaging System ◽  
Acupuncture Point ◽  
Visual Evaluation ◽  
Relative Temperature ◽  
Infrared Thermal Imaging ◽  
Thermal Imaging System ◽  
And Control

This study describes a thermo-visual method to diagnose intracranial hypertension syndrome that is caused by a high intracranial pressure by observing the relative temperature distribution around the "Yin-Tang" acupuncture point. Based on thermo-visual analysis of 3000 thermal images scanned by infrared thermal imaging system acquired from 1256 admitted patients, we found that a certain specific temperature distribution around the Yin-Tang acupuncture point was related with the degree of severity of intracranial hypertension syndrome. Thus, we claim that the evaluation of the relative temperature distribution around the Yin-Tang acupuncture point can be used to diagnose and control intracranial hypertension syndrome during medical treatments.

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