Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition mod...

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Vydané v:Scientific reports Ročník 14; číslo 1; s. 10657 - 14
Hlavní autori: Lin, Lulu, Guo, Jianxian, Liu, Lincheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 09.05.2024
Nature Publishing Group
Nature Portfolio
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Algorithms
Automatic recognition
Automation
Computer vision
Humanities and Social Sciences
Inspection
Machine vision
Monocular vision
multidisciplinary
Neural networks
Optimization algorithms
Positioning technology
Power plants
Robots
Science
Science (multidisciplinary)
Monocular vision
Positioning technology
Machine vision
Automatic recognition
ISSN:2045-2322, 2045-2322
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Shrnutí:As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-56795-8