A Study on Deep CNN Structures for Defect Detection From Laser
  Ultrasonic Visualization Testing Images
Miya Nakajima Takahiro Saitoh Tsuyoshi Kato
The importance of ultrasonic nondestructive testing has been increasing in
recent years, and there are high expectations for the potential of laser
ultrasonic visualization testing, which combines laser ultrasonic testing with
scattered wave visualization technology. Even if scattered waves are
visualized, inspectors still need to carefully inspect the images. To automate
this, this paper proposes a deep neural network for automatic defect detection
and localization in LUVT images. To explore the structure of a neural network
suitable to this task, we compared the LUVT image analysis problem with the
generic object detection problem. Numerical experiments using real-world data
from a SUS304 flat plate showed that the proposed method is more effective than
the general object detection model in terms of prediction performance. We also
show that the computational time required for prediction is faster than that of
the general object detection model.