Research on Detection Method for Welding Seam Defects in Ultrasonic TOFD Image Based on Mask R-CNN

Abstract

Abstract In this paper, the technology of ultrasonic TOFD was used in the welding seam inspection of construction steel structure. 162 ultrasonic TOFD scanning images including five kinds of common defects were obtained through field test. The five kinds of common welding defects include stoma, slag inclusion, lack of fusion, incomplete penetration and crackle. Combined with artificial intelligence image recognition technology, an automatic detection algorithm of welding seam defects in ultrasonic TOFD scanning images based on Mask R-CNN was developed. By comparing the effects of automatic location and classification of welding seam defects based on Mask R-CNN after the same number of iterative training under different parameter configurations, the results show that Mask R-CNN can automatically locate and classify welding seam defects and the recognition effect is the best when epoch is 667, steps_per_epoch is 15 and learning rate is 0.001. Under this parameter configuration, the recognition and classification of five kinds of common defects such as stoma, slag inclusion, lack of fusion, incomplete penetration and crackle are correct. The correct recognition confidence of all five kinds of defects can reach more than 0.9, and the location is precise.