Assessment of importance-based machine learning feature selection methods for aggregate size distribution measurement in a 3D binocular vision system

Abstract

Aggregate size is usually measured by manual sampling and sieving. Machine vision techniques can provide fast, non-invasive measurement. However, the traditional imaging method using a single size descriptor to discriminate different sieve-size classes of coarse aggregates might not yield high-precision classification results. To determine the optimum supervised machine learning model for coarse aggregates sieve-size measurement, 17 methods were evaluated and compared. To train our model, a new dataset named MFCA27 (Multiple Features of Coarse Aggregate 27) was introduced, which contains 27 features of aggregates based on aggregate three-dimensional (3D) top-surface object. In addition, a feature selection approach for investigating how accuracy varied with the datasets under different feature sets was developed, where feature selection was performed according to the impurity-based feature importance score measured using an extremely randomized tree model. Experiments demonstrated that the Gaussian process classifier (GPC) was the best-performing method on the datasets with two- or three-dimensional (2D/3D) feature sets in terms of accuracy and robustness. The results also showed that, compared with the traditional aggregate sieve-size measurement method, which is based on a single size descriptor, GPC can achieve an accuracy of 95.06% on the test dataset of MFCA27 in the aggregate sieve-size class measurement task.