Comparison of Unet and Mask R-CNN for impact damage segmentation in lock-in thermography phase images

Pollyana Gomes Minatel; Bernardo Cassimiro Fonseca de Oliveira; Armando Albertazzi Goncalves Junior

doi:10.1117/12.2600734

20 June 2021 Comparison of Unet and Mask R-CNN for impact damage segmentation in lock-in thermography phase images

Pollyana Gomes Minatel, Bernardo Cassimiro Fonseca de Oliveira, Armando Albertazzi Goncalves Junior

Proceedings Volume 11787, Automated Visual Inspection and Machine Vision IV; 117870S (2021) https://doi.org/10.1117/12.2600734
Event: SPIE Optical Metrology, 2021, Online Only

Conference Poster

Abstract

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.

Citation Download Citation

Pollyana Gomes Minatel, Bernardo Cassimiro Fonseca de Oliveira, and Armando Albertazzi Goncalves Junior "Comparison of Unet and Mask R-CNN for impact damage segmentation in lock-in thermography phase images", Proc. SPIE 11787, Automated Visual Inspection and Machine Vision IV, 117870S (20 June 2021); https://doi.org/10.1117/12.2600734

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