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

by   Javad Manashti, et al.

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 testing dataset and 9.1 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.


page 8

page 10

page 19

page 20

page 21

page 22

page 25


PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks

This project aimed to determine the grain size distribution of granular ...

Combining pretrained CNN feature extractors to enhance clustering of complex natural images

Recently, a common starting point for solving complex unsupervised image...

Feature Extraction via Recurrent Random Deep Ensembles and its Application in Gruop-level Happiness Estimation

This paper presents a novel ensemble framework to extract highly discrim...

A New Color Feature Extraction Method Based on Dynamic Color Distribution Entropy of Neighborhoods

One of the important requirements in image retrieval, indexing, classifi...

Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model

Due to lack of annotated pathological images, transfer learning has been...

Passing Multi-Channel Material Textures to a 3-Channel Loss

Our objective is to compute a textural loss that can be used to train te...

Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images

Targeted color-dots with varying shapes and sizes in images are first ex...

Please sign up or login with your details

Forgot password? Click here to reset