Carbon fiber reinforced plastic (CFRPs) is a composite material that has substituted metal alloys in many industrial fields. Non-destructive testing techniques are interesting inspection methods for the integrity assessment of composite materials and Optical Lock-in Thermography (OLT) is a particularly convenient alternative to inspection because setting different loading frequencies will result in different scanning depths. Regarding the segmentation task, the problem to be solved is to develop a tool that can correctly identify defective areas with several geometric shapes and features even if there is noise, and without using any manual input or creating artifacts in the image. This work describes the application of Unet and Mask R-CNN in the segmentation of defects in OLT phase images of CFRP plates. The output images from the evaluation were compared using the IoU and ANOVA test as a significance evaluator. The results show that Mask R-CNN performed better-segmenting OLT images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.