A thermographic data augmentation and signal separation method for defect detection
Sign Up to like & getrecommendations! Published in 2020 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/abc63f
Abstract: Non-destructive testing is a popular technique for defect assessment of composite materials, where machine learning models become more important in its data analysis. Nevertheless, deep learning, which has achieved state-of-the-art results in many tasks, has… read more here.
Keywords: defect detection; thermographic data; method; data augmentation ... See more keywords
Multiview Learning for Subsurface Defect Detection in Composite Products: A Challenge on Thermographic Data Analysis
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2019.2963795
Abstract: Nondestructive testing (NDT) is an economical way of detecting subsurface defects in composite products. Infrared thermography serves as a popular NDT method due to its high efficiency and low cost. However, defect identification by directly… read more here.
Keywords: subsurface; composite products; thermographic data; multiview learning ... See more keywords
Enhanced CFRP Defect Detection From Highly Undersampled Thermographic Data via Low-Rank Tensor Completion-Based Thermography
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3154786
Abstract: In this article, we present a smooth low-rank tensor completion (SLRTC) based reconstruction algorithm to recover raw thermal image sequences from highly randomly undersampled or small numbers of available thermographic data. The presented algorithm is… read more here.
Keywords: rank tensor; thermographic data; low rank; algorithm ... See more keywords