Semantic instance segmentation using convolutional networks for reconstruction of spatial distribution of material properties

Abstract

The general objective of this research is non‐destructive assessment of the grain size in the metals. The authors suggest a new way of applying neural network technology when the neural network is used for analysis of the ultrasonic structural noise. It was assumed that the signals of ultrasonic structural noise are measured at several frequencies. In order to address structural noise issues, a convolutional neural network is designed to process ultrasonic sensor data, to learn structural noise features and to achieve direct grain size estimation simultaneously. To ensure minimum data gathering of metal samples, the design focuses on neural network with concept of semantic instance segmentation, for data extrapolation. Experimental results show that proposed methods as semantic instance segmentation with combined convolutional and fully connected dense neural networks with classifiers outperform the other single neural networks with original samples with high signal to noise ratio (SN) data.