Abstract
This work tests a self-annotation-based unsupervised methodology for training a convolutional neural network (CNN) model for semantic segmentation of X-ray computed tomography (XCT) scans of concretes. Concrete poses a unique challenge for XCT imaging due to similar X-ray attenuation coefficients of aggregates and mortar, resulting in low-contrast between the two phases in the ensuing images. While CNN-based models are a proven technique for semantic segmentation in such challenging cases, they typically require labeled training data, which is often unavailable for new datasets or are costly to obtain. To counter that limitation, a self-annotation technique is used here which leverages superpixel algorithms to identify perceptually similar local regions in an image and relates them to the global context in the image by utilizing the receptive field of a CNN-based model. This enables the model to learn a global-local relationship in the images and enables identification of semantically similar structures. We therefore present the performance of the unsupervised training methodology on our XCT datasets and discuss potential avenues for further improvements.
Abstract (translated)
这项工作测试了一种基于自我标注的无监督方法，用于训练卷积神经网络（CNN）模型对混凝土X射线计算机断层扫描（XCT）图像进行语义分割。混凝土为XCT成像带来了独特的挑战，因为骨料和砂浆具有相似的X射线衰减系数，在生成的图像中导致两个相之间的对比度较低。虽然基于CNN的模型已被证明在类似困难案例中的语义分割是有效的技术，但它们通常需要标注的训练数据，而这些数据对于新的数据集往往不可用或难以获取。为了克服这一限制，本文采用了一种自我标注技术，该技术利用超像素算法来识别图像中感知上相似的局部区域，并通过基于CNN模型的感受野将它们与图像中的全局上下文相关联。这使得模型能够学习图像中的全局-局部关系，并能够识别语义上类似的结构。因此，我们展示了在我们的XCT数据集上的无监督训练方法的表现，并讨论了进一步改进的可能性途径。
URL
https://arxiv.org/abs/2603.00127
