Automatic Recognition of Weld Defects in TOFD D-Scan Images Based on Faster R-CNN

Abstract

Abstract The type recognition of a weld defect in an ultrasonic Time of Flight Diffraction (TOFD) image is lack of efficiency, stability, and reliability that is due to the limitation of experience and professional knowledge of the inspector. In this study, characteristics of a weld defect of TOFD D-scan images were analyzed, and the Faster Region-based Convolutional Neural Networks (Faster R-CNN) was adopted for autorecognition of the defect’s type. In the course of the training process, the proposed box configuration was optimized to improve training and recognition efficiency, and the imaging samples were expanded for training, adjusting, and verifying the Faster R-CNN. Eventually, the recognition effect and influence factors of misrecognition were analyzed. Research results show that a D-scan image of the weld defect is closely related to the defect’s shape, which can be used to decide the type a weld defect’s type. Automatic recognition of the defect’s type based on the Faster R-CNN possesses the advantages of high recognition rate, robustness, and antijamming ability, which can achieve a recognition accuracy of 80–97 % for the weld defect’s type. In addition, the analysis of misrecognition shows that it is necessary to denoise the D-scan images before autorecognition because, in the light of the noise, stripes are liable to be misrecognized as porosity and cracks in the recognition experiments.