Research Area: Machine Learning
Non-destructive testing is a group of methods for evaluating the integrity of components. Among them, ultrasonic inspection stands out due to its ability to visualize both shallow and deep sections of the material in the search for flaws. Testing of the critical components can be a tiring and time-consuming task. Therefore, human experts in analyzing inspection data could use a hand in discarding anomaly-free data and reviewing only suspicious data. Using such a tool, errors would be less common, inspection times would shorten and non-destructive testing would be more efficient. In this work, we evaluate multiple state-of-the-art deep-learning anomaly detection methods on the ultrasonic non-destructive testing dataset. We achieved an average performance of almost 82% of ROC AUC. We discuss in detail the advantages and disadvantages of the presented methods.
Keywords:
Deep learning
Anomaly detection
ultrasonic images
Author(s) Name: Luka Posilović, Duje Medak, Fran Milković, Marko Subašić, Marko Budimir, Sven Lončarić
Journal name: Ultrasonics
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.ultras.2022.106737
Volume Information: Volume 124, August 2022, 106737
