Thermographic augmentation of defects on thermal images using multi-modal data

Parham Nooralishahi; Kevin Laurent; Fernando Lopez; Xavier Maldague

doi:10.1117/12.2664113

12 June 2023 Thermographic augmentation of defects on thermal images using multi-modal data

Parham Nooralishahi, Kevin Laurent, Fernando Lopez, Xavier Maldague

Proceedings Volume 12536, Thermosense: Thermal Infrared Applications XLV; 1253613 (2023) https://doi.org/10.1117/12.2664113
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

In recent years, many studies have focused on using deep-learning approaches for automatic defect detection in the thermographic inspection of industrial and construction components. Deep Convolutional Neural Networks have proven to perform remarkably on thermal defect detection. However, their convergence and accuracy are heavily associated with having a large amount of training data to avoid overfitting and ensure reliable detection. Unfortunately, the number of available labeled thermal datasets for inspection-related applications is very limited. One of the practical approaches to address this issue is data augmentation. This paper proposes a novel approach for augmenting simulated thermal defects on regions of interest using coupled thermal and visible images. The visible images are employed to extract regions of interest in both modalities using a texture segmentation method. Later, the introduced method is used to augment thermal defects on thermal images.

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Parham Nooralishahi, Kevin Laurent, Fernando Lopez, and Xavier Maldague "Thermographic augmentation of defects on thermal images using multi-modal data", Proc. SPIE 12536, Thermosense: Thermal Infrared Applications XLV, 1253613 (12 June 2023); https://doi.org/10.1117/12.2664113

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