New software architecture for visualization, transformation, and analysis of equipment images
Abstract
The paper considers the problem of designing software for working with optical and radiometric data collected during equipment inspection using cameras of various spectra. Existing software products allow processing and analyzing individual images in various spectra from diagnostic equipment, but do not provide their binding to equipment (for example, through identifiers) and do not assume any data model for storing images. Therefore, it leads to the problem of integrating such software with other enterprise systems (ERP systems, etc.) and the impossibility of using optical and radiometric data for a comprehensive analysis of the equipment technical state, and also complicates the systematization of information. The software architecture proposed in this paper based on a new logical data model. It allows to significantly expand the functionality of automated analysis and visualization of the results of diagnostic imaging of equipment in various spectra, including visible, infrared and ultraviolet; as well as to organize automatic binding of images to units or nodes of equipment. The solution facilitates the implementation of domestic software and robotic systems in industrial enterprises, since the large amounts of data they generate can only be processed using appropriate algorithms and software.
Full Text:
PDF (Russian)References
A. M. Romanov, N. Gyrichidi, M. A. Volokova, S. A. Eroshenko, P. V. Matrenin, and A. I. Khalyasmaa, “ Automated mission planning for aerial large-scale power plant thermal inspection,” Journal of Field Robotics, vol. 41(5), pp. 1313–1348, 2024.
N. Gyrichidi, A. M. Romanov, O. V. Trofimov, S. A. Eroshenko, P. V. Matrenin, and A. I. Khalyasmaa, “GNSS-Based Narrow-Angle UV Camera Targeting: Case Study of a Low-Cost MAD Robot,” Sensiors, vol. 24(11), pp. 3494, 2024.
X. Tao, D. Zhang, W. Ma, X. Liu, and D. Xu, “Automatic Crack Detection for Metallic Surfaces Using Convolutional Neural Networks,” Applied Sciences, vol. 8(9), pp. 1575, 2018.
A. Das, S. Dorafshan, and N. Kaabouch, “Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep Learning,” Sensors, vol. 24(11), pp. 2603, 2024.
H. Zheng et al., “An Infrared Image Detection Method of Substation Equipment Combining Iresgroup Structure and CenterNet,” IEEE Transactions on Power Delivery, vol. 37(6), pp. 4757–4765, 2022.
J. Zhang and W. Zhu, “Research on Algorithm for Improving Infrared Image Defect Segmentation of Power Equipment,” Electronics, vol. 12(7), pp. 1588, 2023.
H. Zheng et al., “Arbitrary-Oriented Detection of Insulators in Thermal Imagery via Rotation Region Network,” IEEE Transactions on Industrial Informatics, vol. 18(8), pp. 5242–5252, 2022.
Y. Zhang, X. Li, and J. Wang, “Automatic Hot Spot Detection in Infrared Images Using Convolutional Neural Networks,” IEEE Transactions on Industrial Electronics, vol. 67(8), pp. 6785–6795, 2020.
A. G. Ovsyannikov and S. M. Korobeynikov, “Kontrol' izolyatsii po chastichnym razryadam [Insulation control by partial discharges (historical background)],” Elektroenergiya. Peredacha i raspredeleniye [Electricity. Transmission and distribution], vol. 2(65), pp. 124–130, 2021.
W. Zhao, W. Liu, Y. Hu, Y. An, and Y. Li, “Extraction method of insulator discharge area in ultraviolet image and its application,” Proceedings of the 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, 2017, pp. 857–961.
D. Zhang and S. Chen, “Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information,” Energies, vol. 13(19), pp. 5221, 2020.
L. Jin and D. Zhang, “Contamination Grades Recognition of Ceramic Insulators Using Fused Features of Infrared and Ultraviolet Images,” Energies, vol. 8(2), pp. 837–858, 2015.
R. Wang and L. Xu, “Line-of-Sight Temperature Profile Reconstruction of Axisymmetric Laminar Flame by Multispectral Dispersion Spectroscopy,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 7008108, 2024.
S. -Y. Cao, H. -L. Shen, S. -J. Chen and C. Li, “Boosting Structure Consistency for Multispectral and Multimodal Image Registration,” IEEE Transactions on Image Processing, vol. 29, pp. 5147–5162, 2020.
M. F. Meyer, J. A. Gonçalves, and A.M.F. Bio, “Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation,” Remote Sensing, vol. 16(4), pp. 652. 2024.
N. Sharma, B. P. Banerjee, M. Hayden, and S. Kant. “An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse,” Plants, vol. 12(12), pp. 317, 2023.
L. Wunsch, M. Hubold, R. Nestler, G. Notni, “Realisation of an Application Specific Multispectral Snapshot-Imaging System Based on Multi-Aperture-Technology and Multispectral Machine Learning Loops,” Sensors, vol. 24(24), pp. 7984, 2024.
R. C. Martin, Agile Software Development, Principles, Patterns, and Practices, New Jersey:Prentice Hall, 2003.
Refbacks
- There are currently no refbacks.
Abava Кибербезопасность ИБП для ЦОД СНЭ
ISSN: 2307-8162