Article
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Preserved in Portico This version is not peer-reviewed
Pulsed Thermography Dataset for Training Deep Learning Models
Version 1
: Received: 25 January 2023 / Approved: 26 January 2023 / Online: 26 January 2023 (17:11:04 CET)
A peer-reviewed article of this Preprint also exists.
Wei, Z.; Osman, A.; Valeske, B.; Maldague, X. Pulsed Thermography Dataset for Training Deep Learning Models. Appl. Sci. 2023, 13, 2901. Wei, Z.; Osman, A.; Valeske, B.; Maldague, X. Pulsed Thermography Dataset for Training Deep Learning Models. Appl. Sci. 2023, 13, 2901.
Abstract
Pulsed thermography is a vital technique in the nondestructive evaluation field. However, its data analysis can be complex and requires skilled experts. Advances in deep learning have yielded exceptional results, including image segmentation. Therefore, many efforts have been made to apply deep learning methods to data processing for nondestructive evaluation. Despite this, there is currently no public Pulsed thermographic dataset available for evaluating various spatial-temporal deep methods of segmenting pulsed thermographic data. This article aims to provide such a dataset and assess the performance of commonly used deep learning-based instance segmentation models on it. Additionally, the impact of the number of frames and data transformations on model performance is examined. The findings suggest that suitable preprocessing methods can effectively reduce the data size without compromising the deep models’ performance.
Keywords
Pulsed thermography; Deep learning; Defect detection; Nondestructive evaluation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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