Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review

Non-destructive testing (NDT) techniques play a crucial role in industrial production, aerospace, healthcare, and the inspection of special equipment, serving as an indispensable part of assessing the safety condition of pressure equipment. Among these, the analysis of NDT data stands as a critical...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 24; číslo 18; s. 5944
Hlavní autoři: Wang, Weihua, Chen, Jiugong, Han, Gangsheng, Shi, Xiushan, Qian, Gong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.09.2024
MDPI
Témata:
Acoustic emission testing
Algorithms
Automation
Computational linguistics
Corrosion
Deep learning
Grain size
Language processing
Medical imaging equipment
Natural language interfaces
Neural networks
non-destructive testing (NDT)
object detection
pressure equipment
Review
New York
deep learning
object detection
non-destructive testing (NDT)
pressure equipment
ISSN:1424-8220, 1424-8220
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Shrnutí:Non-destructive testing (NDT) techniques play a crucial role in industrial production, aerospace, healthcare, and the inspection of special equipment, serving as an indispensable part of assessing the safety condition of pressure equipment. Among these, the analysis of NDT data stands as a critical link in evaluating equipment safety. In recent years, object detection techniques have gradually been applied to the analysis of NDT data in pressure equipment inspection, yielding significant results. This paper comprehensively reviews the current applications and development trends of object detection algorithms in NDT technology for pressure-bearing equipment, focusing on algorithm selection, data augmentation, and intelligent defect recognition based on object detection algorithms. Additionally, it explores open research challenges of integrating GAN-based data augmentation and unsupervised learning to further enhance the intelligent application and performance of object detection technology in NDT for pressure-bearing equipment while discussing techniques and methods to improve the interpretability of deep learning models. Finally, by summarizing current research and offering insights for future directions, this paper aims to provide researchers and engineers with a comprehensive perspective to advance the application and development of object detection technology in NDT for pressure-bearing equipment.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24185944