Laser flying-spot thermography: an open-access dataset for machine learning and deep learning

Abstract

“Flying spot” laser infrared thermography (FST) is a non destructive testing technique able to detect small defects by scanning surfaces with a laser heat source. Defects, such as cracks on metallic parts, are revealed by the disturbance of heat propagation measured by an infrared camera. Deep learning approaches are now very efficient to automatically analyse and use contextual information from data, and can be used for crack detection. However, in the literature only few works deal with the use of deep learning for the crack detection in FST. Indeed obtaining a large amount of data from FST examinations can be expensive and time-consuming. We propose here to build a generic, open-access dataset of laser thermography for defect detection. This database can be used by the community to develop new crack detection methods that can be benchmarked on the same database, as well as for pretraining networks for similar application tasks. We also present results of state of the art detection networks trained with the proposed database. These models give a basis for future works. Dataset, called FLYD (FLYing spot thermography Dataset), will be available in : https://github.com/kevinhelvig/FLYD/.