论文标题
基于深度学习的自动图像分割，用于具体岩石学分析
Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis
论文作者
论文摘要
用于测量混凝土中空气空隙的标准岩石学测试方法（ASTM C457）需要对立体显微镜下样品相组成的细致检查。高的专业知识和专业设备阻止此测试进行常规的混凝土质量控制。尽管可以借助基于颜色的图像分割来缓解任务，但仍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实现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混凝土分割的可行性。 CNN表现出强大的处理多种混凝土的潜力，包括不参与模型训练的混凝土。实验结果表明，CNN的表现优于基于颜色的分割，并且与人类专家具有可比的精度。此外，分割时间减少到仅秒。
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.
