Database for transfer learning in crack detection and localization on metallic materials using flying spot thermography and deep learning

Abstract

Flying spot" laser infrared thermography (FST) is a non destructive testing technique able to detect 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 analyze 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. This work presents an open-access database for "flying spot" laser thermography, annotated for crack-type defect localization. It is followed by a benchmark of several state-of-the-art machine learning architectures. The benefits of this database for transfer to the detection and localization of cracks in coated metallic samples are highlighted, with very reduced amounts of data. Finally, this work gives a preliminary exploration on the use of individual thermal images, yielding to detection and localization performance comparable to the previously studied reconstructed thermal maps, which consists in the normalized mean of all the thermal images on a region of interest, after registration.