Estimation of limestone particle mass from 2D images

Abstract

The strength of asphalt pavements is largely determined by the distribution of particle sizes (gradation) and shapes in the aggregate used in the mixture. Currently, these parameters are determined by manual sampling. The manual method is time-consuming and cannot provide real-time feedback for process control purposes. In this paper, an approach for predicting particle mass based on 2D electronic images is described. Crushed limestone aggregates, similar to those used in asphalt pavement mixtures were placed on a light table and imaged using a CCD video camera and framegrabber. The images were processed to separate touching and overlapping particles, define the edges of the particles and to calculate certain features of the particle silhouettes, such as area, centroid and shape-related features. Several dimensionless parameters were defined, based on the image features. A multiple linear regression model was created, using the dimensionless parameters as regressor variables to predict particle mass. Regressor coefficients were found by fitting to a sample of 501 particles ranging in size from 4.75 mm<particle sieve size<25 mm. When tested against a different aggregate sample, the model predicted the mass of the batch to within ±2%.