Segmentation of backscattered electron (BSE) images of cementitious materials is often used to quantify different microstructural features for the sake of performance estimation at macro-scale levels. However, the heterogeneous nature of cementitious matrices compounds with varying imaging conditions can lead the conventional segmentation methods to a processing bottleneck for largescale experiments. To overcome these challenges, in this study, we evaluate the potential of deep autoencoder convolutional networks, specifically SegNet, for automatic segmentation of fly ash-based geopolymer images. We present the SegNet power in achieving a comparable accuracy to the human performance even with a few BSE images in the model’s training. The SegNet demonstrates magnification independent training that enables test image processing with both seen and unseen magnification levels. A comparative study shows that SegNet outperforms the Gaussian method on uncontrolled imaging conditions such as background brightness levels. In addition, we demonstrate the self-learning capability of SegNet in poorly annotated areas.
- Backscattered electron imaging
- Deep learning
- Convolutional neural networks