Unmanned Aerial Vehicles and Artificial Intelligence Technologies as a Tool for Automating of Thermal Power Plant Boiler Monitoring

被引：0
|
作者：
Kalyagin, M. Yu. [1 ]
Vititin, V.F. [1 ]
Kondarattsev, V.L. [1 ]
机构：
[1] Moscow Aviation Institute, Moscow,125993, Russia
来源：
Russian Engineering Research | 2024年 / 44卷 / 08期
关键词：
Abstract: Approaches to creating an automated system for monitoring the inner surface of a thermal power plant boiler using small-sized unmanned aerial vehicles; neural networks; and computer vision technologies are considered. Analysis of visual defects in boiler tubes made it possible to identify five main types of defects; for each of them datasets are created using augmentation and synthetic data generation procedures. Three neural networks (YOLOv4; DetectoRS; and DCN) are trained using the datasets. Their characteristics are determined experimentally; and a comparative analysis of the reliability and speed of defect detection is carried out. © Allerton Press; Inc; 2024; ISSN; 1068-798X; Russian Engineering Research; Vol; 44; No; 8; pp. 1215–1219. Allerton Press; 2024. Russian Text The Author(s); published in STIN; 7; pp; 48–52;
D O I：
10.3103/S1068798X24701971
中图分类号：
学科分类号：
摘要：
引用
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页码：1215 / 1219
页数：4
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