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.

Weld region salient object extraction based on MC-U2Net

“…In fact, welding seam cutting is also a typical image processing task that can be attempted using deep learning methods for segmentation. Jin et al [11]used U-Net for welding seam cutting and achieved an mIOU of 85.44% on the test set. Golodov et al [12]used the FgSegNet_v2 model for the welding seam area extraction task and also achieved good recognition results.…”

RIIAnet: A Real-Time Segmentation Network Integrated with Multi-Type Features of Different Depths for Pavement Cracks

“…With the development of deep learning technology, the ability of computer systems to recognize and process images has been greatly improved because of the use of DCNNs and some image techniques using deep learning [14,[16][17][18] are developed to detect cracks. The FCN [19] network is the first end-to-end semantic segmentation network that uses a fully convolutional neural network (FCN) architecture, and performs dense execution based on 1 × 1 convolution to reduce parameters and improve speed.…”