Deep Learning-Based Identification of Cracks Using Ultrasonic Phased-Array Images

Abstract

In order to realize the automatic recognition and classification of cracks with different depths, in this study, several deep convolutional neural networks including AlexNet, ResNet, and DenseNet were employed to identify and classify cracks at different depths and in various materials. An analysis process for the automatic classification of crack damage was presented. The image dataset used for model training was obtained from scanning experiments on aluminum and titanium alloy plates using an ultrasonic phased-array flaw detector. All models were trained and validated with the dataset; the proposed models were compared using classification precision and loss values. The results show that the automatic recognition and classification of crack depth can be realized by using the deep learning algorithm to analyze the ultrasonic phased array images, and the classification precision of DenseNet is the highest. The problem that ultrasonic damage identification relies on manual experience is solved.