Estimating particle size distributions based on machine vision

Sammanfattning: This thesis contributes to the field of machine vision and the theory of the sampling of particulate material on conveyor belts. The objective is to address sources of error relevant to surface-analysis techniques when estimating the sieve-size distribution of particulate material using machine vision. The relevant sources of error are segregation and grouping error, capturing error, profile error, overlapping-particle error and weight-transformation error. Segregation and grouping error describes the tendency of a pile to separate into groups of similarly sized particles, which may bias the results of surface-analysis techniques. Capturing error describes the varying probability, based on size, that a particle will appear on the surface of the pile, which may also bias the results of surface-analysis techniques. Profile error is related to the fact that only one side of an entirely visible particle can be seen, which may bias the estimation of particle size. Overlapping-particle error occurs when many particles are only partially visible, which may bias the estimation of particle size because large particles may be treated as smaller particles. Weight-transformation error arises because the weight of particles in a specific sieve-size class might significantly vary, resulting in incorrect estimates of particle weights. The focus of the thesis is mainly on solutions for minimizing profile error, overlapping-particle error and weight-transformation error.In the aggregates and mining industries, suppliers of particulate materials, such as crushed rock and pelletised iron ore, produce materials for which the particle size is a key differentiating factor in the quality of the material. Manual sampling and sieving techniques are the industry-standard methods for estimating the size distribution of these particles. However, as manual sampling is time consuming, there are long response times before an estimate of the sieve-size distributions is available. Machine-vision techniques promise a non-invasive, frequent and consistent solution for determining the size distribution of particles. Machine-vision techniques capture images of the surfaces of piles, which are analyzed by identifying each particle on the surface of the pile and estimating its size. Sampling particulate material being transported on conveyor belts using machine vision has been an area of active research for over 25 years. However, there are still a number of sources of error in this type of sampling that are not fully understood. To achieve a high accuracy and robustness in the analysis of captured surfaces, detailed experiments were performed in the course of this thesis work, towards the development and validation of techniques for minimizing overlapping-particle error, profile error and weight-transformation error. To minimise overlapping-particle error and profile error, classification algorithms based on logistic regression were proposed. Logistic regression is a statistical classification method that is used for visibility classification to minimize overlapping-particle error and in particle-size classification to minimize profile error. Commonly used size- and shape-measurement methods were evaluated using feature-selection techniques, to find sets of statistically significant features that should be used for the abovementioned classification tasks. Validation using data not used for training showed that these errors can be overcome.The existence of an effect that causes weight-transformation error was identified using statistical analysis of variance (ANOVA). Methods to minimize weight-transformation error are presented herein, and one implementation showed a good correlation between the results using the machine-vision system and manual sieving results.The results presented in this thesis show that by addressing the relevant sources of error, machine vision techniques allow for robust and accurate analysis of particulate material. An industrial prototype was developed that estimates the sieve-size distribution of iron-ore pellets in a pellet plant and crushed limestone in a quarry during ship loading. The industrial prototype also enables product identification of crushed limestone to prevent the loading of incorrectly sized products.