Comparing PSDNet, pretrained networks, and traditional feature extraction for predicting the particle size distribution of granular materials from photographs

Abstract: 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.