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A platform for research: civil engineering, architecture and urbanism
How to classify sand types: A deep learning approach
Abstract 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.
Highlights Convolutional neural networks can satisfactorily classify multiple sand types. The Inception architecture outperforms the VGG and ResNet architectures in the classification task. Both particle shape and surface texture are the keys to improving the accuracy.
How to classify sand types: A deep learning approach
Abstract 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.
Highlights Convolutional neural networks can satisfactorily classify multiple sand types. The Inception architecture outperforms the VGG and ResNet architectures in the classification task. Both particle shape and surface texture are the keys to improving the accuracy.
How to classify sand types: A deep learning approach
Kim, Yejin (author) / Yun, Tae Sup (author)
Engineering Geology ; 288
2021-04-12
Article (Journal)
Electronic Resource
English
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