# Extracting Weld Bead Shapes from Radiographic Testing Images with U-Net
## 5 Conclusions  
To efficiently inspect and determine defects and inadequate of the weld zone, a method of extracting only the weld bead from radiographic testing image was conducted.  
The contrast of the weld image was improved using CLAHE as an image preprocessing operation, so that the weld zone became more prominent. Additionally, by performing data augmentation, 500 weld images were expanded to a total of 2500 images. The U-Net deep learning algorithm was used as an algorithm for extracting weld beads. After the training, the performance of the three models was compared.  
The mIoU values for training and test data derived from model trained with images applied with CLAHE and data augmentation were 90.58% and 85.44%, respectively, and those for training and test data derived from model trained with images applied with only CLAHE were 89.93% and 84.88%, respectively. On the other hand, the mIoU values for training and test data derived from model with raw images were 89.10% and 83.47%, showing the lowest values. The deep learning performance was improved by applying CLAHE and data augmentation.  
In the radiographic testing images, information about the image as well as weld bead was marked. Additionally, although the proportion of weld bead in the image is not large, the weld bead could be effectively extracted as a result of image post-processing based on the trained results.  
The method presented in this study was compared with the results of general image segmentation methods. Since general image segmentation methods determined that beads and text were the same area in the image, it was difficult to extract only beads. In contrast, it was confirmed that U-Net could extract only beads from images and produced better results than other image segmentation methods.  
Five hundred radiographic testing images were used in the experiment, and a higher degree of accuracy for a wider variety of weld image types can be achieved by procuring additional images in the future. Additionally, it will be possible to reduce the time and cost of the weld quality inspections by automating the extraction of the weld bead shapes.  
Conceptualization, G.-s.J., S.-O., Y.-s.L. and S.-c.S.; methodology, G.-s.J., S.-j.O., Y.-s.L. and S.-c.S.; validation, G.-s.J., S.-j.O., Y.-s.L. and S.-c.S.; formal analysis, G.-s.J., Y.-s.L. and S.-c.S.; investigation, G.-s.J. and S.-j.O.; writing--original draft preparation, G.-s.J.; writing--review and editing, G.-s.J. and S.-j.O.; supervision, Y.-s.L. and S.-c.S.; project administration, Y.-s.L. and S.-c.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This paper was written with the support of the Korea Institute for Advancement of Technology funded by the Ministry of Trade and Industry in 2021, the \"Autonomous Ship Technology Development Project (20200615)\".  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **IoU** & **mIoU** & **Accuracy** \\ \hline (**a**) & 20.06\% & 40.22\% & 44.18\% \\ \hline (**b**) & 18.34\% & 29.93\% & 38.72\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of comparing with the true mask image set: (**a**) the result of images in which k-means is performed; (**b**) the result of images in which thresholding is performed.