Combination of thermal fundamentals and Deep Learning for infrastructure inspections from thermographic images. Preliminary results
UNIVERSAL IDENTIFIER: http://hdl.handle.net/11093/1687
EDITED VERSION: http://qirt.org/archives/qirt2020/papers/044.pdf
UNESCO SUBJECT: 3311.02 Ingeniería de Control
DOCUMENT TYPE: conferenceObject
The application of Deep Learning (DL) models using the measurements acquired by Non-Destructive Testing (NTD) tools as input data stands as a versatile solution for highly automated analysis. However, DL models using thermal images as input data are quite scarce when it comes to analysing defects in medium- and large-scale bodies. Therefore, this paper proposes the application of a thermal criterion and a DL model, Mask R-CNN, in thermal images acquired from different infrastructures with thermal bridges and moisture. The thermal criterion is first applied to the input data, showing its utility to improve DL models performance
Files in this item
- GarridoGonzalez_Ivan_QIRT_2020 ...