Pulsed Thermography Dataset for Training Deep Learning Models

Pulsed thermography is an indispensable tool in the field of non-destructive evaluation. However, the data generated by this technique can be challenging to analyze and require expertise to interpret. With the rapid progress in deep learning, image segmentation has become a well-established area of research. This has motivated efforts to apply deep learning methods to non-destructive evaluation data processing, including pulsed thermography. Despite this trend, there has been a lack of public pulsed thermography datasets available for the evaluation of various spatial-temporal deep learning models for segmentation tasks. This paper aims to address this gap by presenting the PVC-Infrared dataset for deep learning. In addition, we evaluated the performance of popular deep-learning-based instance segmentation models on this dataset. Furthermore, we examined the effect of the number of frames and data transformations on the performance of these models. The results of this study suggest that appropriate preprocessing techniques can significantly reduce the size of the data while maintaining the performance of deep learning models, thereby speeding up the data processing process. This highlights the potential for using deep learning methods to make non-destructive evaluation data analysis more efficient and accessible to a wider range of practitioners.

Automated summary

Pulsed thermography is an essential tool in non-destructive evaluation, but processing the data it generates can be challenging. This study presents the PVC-Infrared dataset and evaluates the performance of popular deep learning models on this dataset, addressing a gap in non-destructive evaluation data processing. The results show that appropriate preprocessing techniques can significantly reduce data size while maintaining the performance of deep learning models, highlighting the potential for more efficient and accessible non-destructive evaluation data analysis using deep learning methods.