Applying SDR with CNN to Identify Weld Defect: A New Processing Method

Abstract

X-ray weld images are investigated to achieve nondestructive identification of defects in oil and gas pipeline welds. The content mainly includes image filtering and enhancement, weld defect suspected defect region (SDR) segmentation, and defect identification. (1) The x-ray image quality is poor, as shown by a lot of noise and low contrast. Therefore, the mean filtering method is used to filter out the noise, and a nonlinear enhancement using sin function is proposed to improve the contrast between the weld and the background. (2) In the image segmentation, a segmentation method of SDR is proposed. Region of interest (ROI) is first extracted, and then SDRs are segmented using clustering. (3) In defect identification, deep learning networks are used for SDRs. A 6-stage, 10-layer convolutional neural network (CNN) network structure is designed, and the convolutional kernels are set to be 5×5 and 3×3. In the experiments, the proposed method is able to automate the processing of x-ray weld images and improve the processing efficiency. The identification accuracy rate is 98.9%.