Abstract
ABSTRACT Deep learning has recently gained significant attention in nondestructive testing tasks due to its ability to effectively analyze complex data and identify patterns. This paper proposes a methodology for defect detection on an X-ray computed tomography dataset utilizing the Mask R-CNN algorithm. The proposed approach generates artificial data from the original dataset and introduces a metric for classifying the intensity of the defects. The Mask R-CNN is trained for defect localization and classification, and the results show that the proposed approach performs competitively with, and in many cases better than, reported papers on the same dataset. This paper provides a detailed analysis of the proposed methodology, including experimental results and discussions, demonstrating its effectiveness and potential for future research.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.