  This study aims to evaluate PSDNet, a series of convolutional neural networks
(ConvNets) trained with photographs to predict the particle size distribution
of granular materials. Nine traditional feature extraction methods and 15
pretrained ConvNets were also evaluated and compared. A dataset including 9600
photographs of 15 different granular materials was used. The influence of image
size and color band was verified by using six image sizes between 32 and 160
pixels, and both grayscale and color images as PSDNet inputs. In addition to
random training, validation, and testing datasets, a material removal method
was also used to evaluate the performances of each image analysis method. With
this method, each material was successively removed from the training and
validation datasets and used as the testing dataset. Results show that a
combination of all PSDNet color and grayscale features can lead to a root mean
square error (RMSE) on the percentages passing as low as 1.8 % with a random
testing dataset and 9.1% with the material removal method. For the random
datasets, a combination of all traditional features, and the features extracted
from InceptionResNetV2 led to RMSE on the percentages passing of 2.3 and 1.7 %,
respectively.
