# Extracting Weld Bead Shapes from Radiographic Testing Images with U-Net
###### Abstract  
Metals created by melting basic metal and welding rods in welding operations are referred to as weld beads. The weld bead shape allows the observation of pores and defects such as cracks in the weld zone. Radiographic testing images are used to determine the quality of the weld zone. The extraction of only the weld bead to determine the generative pattern of the bead can help efficiently locate defects in the weld zone. However, manual extraction of the weld bead from weld images is not time and cost-effective. Efficient and rapid welding quality inspection can be conducted by automating weld bead extraction through deep learning. As a result, objectivity can be secured in the quality inspection and determination of the weld zone in the shipbuilding and offshore plant industry. This study presents a method for detecting the weld bead shape and location from the weld zone image using image preprocessing and deep learning models, and extracting the weld bead through image post-processing. In addition, to diversify the data and improve the deep learning performance, data augmentation was performed to artificially expand the image data. Contrast limited adaptive histogram equalization (CLAHE) is used as an image preprocessing method, and the bead is extracted using U-Net, a pixel-based deep learning model. Consequently, the mean intersection over union (mIoU) values are found to be 90.58% and 85.44% in the train and test experiments, respectively. Successful extraction of the bead from the radiographic testing image through post-processing is achieved.  
deep learning; image segmentation; weld bead +
Footnote †: journal: Medical  
subjective and made through experience, resulting in inconsistent and irrational results. If deep learning could be used to automate the extraction of the weld bead shape, it would save considerable time and money, and efficient quality inspections could be conducted. As a result, objectivity can be secured in the quality inspection and determination of the weld zone in the shipbuilding and offshore plant industry. Depending on the bead and base metal generated in the welded area during a welding operation, areas with the same shape may be classified as different types of defects. Therefore, when determining the weld quality, it is necessary to efficiently perform inspections and determine whether the weld zone has defects or inadequate quality by extracting only the bead from the weld image and identifying the bead's generation pattern.  
Algorithms that extract only weld beads from the radiographic testing image have been used to perform weld detection via image preprocessing; however, they have been inefficient owing to noise outside beads that occurs via thresholding, which requires the algorithms to perform the additional task of removing the noise [3;4]. Additionally, image preprocessing methods including noise reduction, contrast enhancement, thresholding, and labeling, were performed to segment only bead from the radiographic testing image [5]. In the image where thresholding was performed, the boundary of the bead area was defined and the edge was extracted to segment only the bead area [6;7]. Attempts to extract only beads from the radiographic testing image are continuously being made in various ways. However, since several tasks are performed manually, efficient tasks are not performed. It is difficult to say that the weld bead was extracted automatically. Therefore, it takes a lot of time and money to extract the weld bead and detect weld defects. This study aims to automatically segment the shape and location of the weld bead from the radiographic testing image through deep learning and the extracting of only the weld bead. To accurately detect weld defects, the weld bead must be extracted from the radiographic testing image, and this task can be performed efficiently if the weld bead extraction is automated.