Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis
This addresses the need for faster, less expert-dependent concrete quality control, though it is incremental as it builds on existing deep learning methods for segmentation.
The study tackled the problem of automating concrete petrographic analysis by using a CNN for image segmentation without color treatment, achieving comparable accuracy to human experts and reducing segmentation time to seconds.
The standard petrography test method for measuring air voids in concrete (ASTM C457) requires a meticulous and long examination of sample phase composition under a stereomicroscope. The high expertise and specialized equipment discourage this test for routine concrete quality control. Though the task can be alleviated with the aid of color-based image segmentation, additional surface color treatment is required. Recently, deep learning algorithms using convolutional neural networks (CNN) have achieved unprecedented segmentation performance on image testing benchmarks. In this study, we investigated the feasibility of using CNN to conduct concrete segmentation without the use of color treatment. The CNN demonstrated a strong potential to process a wide range of concretes, including those not involved in model training. The experimental results showed that CNN outperforms the color-based segmentation by a considerable margin, and has comparable accuracy to human experts. Furthermore, the segmentation time is reduced to mere seconds.