Application of Deep Learning in Infrared Non-Destructive Testing

Abstract

Convolutional Neural Networks (CNN) is already known as strong tools in various fields particularly in image processing and computer vision. This paper aims to exploit the power of CNN for transform learning and utilizes it as an unsupervised feature extractor for analyzing defects in a steel specimen with float batten holes and Carbon Fiber Reinforced Plastic (CFRP) composite materials. A pre-trained CNN (ImageNet-VGG-f) has been used for extraction of the vectorized features along with a spectral angler mapper (SAM) to provide a score for defects presented in the image. Empirical results on two aforementioned datasets indicate a promising performance for application of heating and cooling based active thermography with a reasonable computational cost due to unsupervised nature of the algorithm.