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.

中文翻译：

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用于混凝土岩相分析的基于深度学习的自动图像分割

测量混凝土中气孔的标准岩相学测试方法 (ASTM C457) 需要在立体显微镜下对样品相组成进行细致和长时间的检查。高专业知识和专业设备不鼓励将这种测试用于常规混凝土质量控制。尽管可以借助基于颜色的图像分割来减轻任务，但需要额外的表面颜色处理。最近，使用卷积神经网络 (CNN) 的深度学习算法在图像测试基准上取得了前所未有的分割性能。在这项研究中，我们研究了使用 CNN 在不使用颜色处理的情况下进行具体分割的可行性。CNN 展示了处理各种混凝土的强大潜力，包括那些未参与模型训练的混凝土。实验结果表明，CNN 在很大程度上优于基于颜色的分割，并且具有与人类专家相当的准确性。此外，分割时间减少到仅仅几秒钟。