# Pulsed Thermography Dataset for Training Deep Learning Models
## 6 Conclusions  
In this study, we introduce the PVC-Infrared dataset, a dataset designed specifically for the detection of defects using PT as an NDE method. The purpose of this dataset is to further advance the research in this field and contribute to the growth of deep learning technologies. We have performed experiments with various deep learning models, including U-Net, SegNet, and their variants with a VGG network as a backbone. These experiments are conducted on both the original dataset and on transformed datasets that are processed using PPT and PCT techniques. Additionally, we have explored the impact of using only the leading thermal frames of the PT measurement. The results show that utilizing fewer frames can lead to comparable or even better performance compared to using the entire dataset. It opens up opportunities for improving the efficiency of deep learning methods  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **1810** & **200** & **140** & **100** & **50** & **25** \\ \hline U-Net & 0.748 & 0.762 & 0.729 & 0.728 & 0.705 & 0.467 \\ SegNet & 0.729 & 0.769 & 0.730 & 0.727 & 0.723 & 0.478 \\ \hline \end{tabular}
\end{table}
Table 3: Results (Average IoU) with only leading frames of thermal sequence on the validation dataset. The original measurement contains 1810 frames.  
for PT data processing in industrial settings. In future research, it is important to consider the depth information of defects and evaluate the effectiveness of AI models on PT datasets with defects of varying shapes. Additionally, the exploration of different frame selection methods can lead to improvements in the efficiency of defect segmentation models. By considering these factors, further research can enhance the accuracy and robustness of AI models for defect detection in PT data.  
Z.W.: methodology, data labeling, training the models and original drafting of the manuscript. A.O.: supervision, expertise in AI, manuscript review and editing. B.V.: supervision, funding acquisition, manuscript review and editing. X.M.: supervision, manuscript review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the European Regional Development Fund (ERDF).  
The PT dataset used in the paper can be accessed via the URL [[https://www.kaggle.com/datasets/ziangwei/irtpvc](https://www.kaggle.com/datasets/ziangwei/irtpvc)]([https://www.kaggle.com/datasets/ziangwei/irtpvc](https://www.kaggle.com/datasets/ziangwei/irtpvc)) (accessed on 23 February 2023). The dataset is available for academic and research use only. Papers using this dataset are kindly requested to refer to this paper.  
Support from Fraunhofer IZFP is gratefully acknowledged. [PERSON] is acknowledged for the design and preparation of the test specimen used in the paper within their research on the evaluation of deep learning approaches for quantitative defect testing and reconstruction with thermography. [PERSON] is acknowledged for providing advice for the experimental setup.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ PT & Pulsed Thermography \\ PPT & Pulsed Phase Thermography \\ PCT & Principal component Thermography \\ TSR & Thermographic Signal Reconstruction \\ NDE & Nondestructive Evaluation \\ IoU & Intersection over Union \\ CFRP & Carbon Fiber Reinforced Polymers \\ \end{tabular}