Segmentation of backscattered electron images of cement-based materials using lightweight U-Net with attention mechanism (LWAU-Net)

Abstract

Backscattered electron (BSE) image segmentation is used to characterise the microstructure of cement-based materials to deepen the understanding of macroscopic properties. However, processing bottlenecks have been encountered when using conventional segregation methods for cement-based materials with complex imaging conditions, and it is often necessary to manually adjust the parameters in image processing. This paper used a lightweight U-shaped convolutional neural network with an attention mechanism (LWAU-Net) to segment the backscattered electron images of cement-based materials, and the results of different attention modules were investigated. An augmentation method combining jigsaw transform and piecewise affine is also proposed. The presented segmentation method can more completely segment the hydrated cement, unhydrated cement, and pore regions under different sampling conditions. It achieves high-precision recognition by using lightweight models. The paper finally quantifies the degree of cement hydration and proves the feasibility of the proposed method.