University Laval Infrared Thermography Databases for Deep Learning Multiple Types of Defect Detections Training †

Abstract

Nowadays, automatic defect detection research by deep learning algorithms plays a crucial role, especially for non-destructive evaluation with infrared thermography. In deep learning research, the databases are the Achilles’ heel during the training in order to preserve optimized performance. In this work, we will present the infrared thermography sequences databases from the Universite Laval Multipolar Infrared Vision Infrarouge Multipolaire (MIVIM) research group for regular and irregular defect analysis in order to provide the best data collection resources for the pretraining of convolutional neural network and feature extraction analysis with future researchers and engineers. The databases will include infrared thermography sequences from regular and irregular defects of carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymer (GFRP), plexiglass, aluminum, and steel, which could be available online for public use and research purposes.