  This project aimed to determine the grain size distribution of granular
materials from images using convolutional neural networks. The application of
ConvNet and pretrained ConvNet models, including AlexNet, SqueezeNet,
GoogLeNet, InceptionV3, DenseNet201, MobileNetV2, ResNet18, ResNet50,
ResNet101, Xception, InceptionResNetV2, ShuffleNet, and NASNetMobile was
studied. Synthetic images of granular materials created with the discrete
element code YADE were used. All the models were trained and verified with
grayscale and color band datasets with image sizes ranging from 32 to 160
pixels. The proposed ConvNet model predicts the percentages of mass retained on
the finest sieve, coarsest sieve, and all sieves with root-mean-square errors
of 1.8 %, 3.3 %, and 2.8 %, respectively, and a coefficient of determination of
0.99. For pretrained networks, root-mean-square errors of 2.4 % and 2.8 % were
obtained for the finest sieve with feature extraction and transfer learning
models, respectively.
