While the identification of sand type helps naturally approximate physical and mechanical properties, it is challenging to judge sand types without prior information. This study attempts to identify the sand type in 2D grayscale images by using convolutional neural networks (CNNs). Six different sand samples with high geometric similarity were selected, and individual particle images were taken. Three pretrained networks (VGGNet, ResNet, and Inception) were implemented for retraining with parameter fine-tuning. The results show that most round and irregularly shaped sands are well classified with higher accuracy than sand samples with intermediate shape parameters. Additionally, it is confirmed that the feature maps obtained from multiple layers of trained CNNs sufficiently include the image characteristics of each sand particle. Misclassified particles are mostly found where the shape parameters distributions overlap. Higher accuracy is achieved by using grayscale images for training than using binary images. It implies that a better prediction can be produced when both surface texture and boundary morphology are concurrently trained. This study suggests the strong possibility of classifying sand types and further estimating soil properties only with images.

虽然识别砂型有助于自然地近似物理和机械性能，但要在没有先验信息的情况下判断砂型具有挑战性。这项研究试图通过使用卷积神经网络（CNN）识别2D灰度图像中的沙子类型。选择了六个具有高度几何相似性的不同砂样品，并拍摄了单独的颗粒图像。实施了三个预训练的网络（VGGNet，ResNet和Inception），用于通过参数微调进行再训练。结果表明，与具有中间形状参数的砂样品相比，大多数圆形砂和不规则形状砂的分类精度更高。另外，可以确认，从多层训练的CNN图层获得的特征图足以包含每个沙粒的图像特征。错误分类的粒子通常出现在形状参数分布重叠的地方。通过使用灰度图像进行训练比使用二进制图像获得更高的精度。这意味着当同时训练表面纹理和边界形态时，可以产生更好的预测。这项研究表明仅通过图像就可以对砂类型进行分类并进一步估算土壤性质。