Effects of flow rates and layer thicknesses for aggregate conveying process on the prediction accuracy of aggregate gradation by image segmentation based on machine vision

Abstract The objective of this study was to determine the effects of flow rates and layer thicknesses for the aggregates on the estimation of the aggregate gradation of an asphalt mixture based on machine vision. The watershed algorithm based on distance transform and concave point detection algorithm were synthetically used to split the touching particles. Increasing the flow rate overall decreased the prediction accuracy of aggregate gradation by image segmentation; the prediction accuracy decreased with the increase of the layer thickness. The prediction accuracy of the segmentation was better than that of the online detection of the asphalt mixture plant.

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