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

• Published in: Construction & building materials Vol. 222; pp. 566 - 578
• Main Authors: Cao, Rongji, Zhao, Yulong, Gao, Ying, Huang, Xiaoming, Zhang, Lili
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 20-10-2019
• Subjects: Aggregate; Flow rate; Layer thickness; Machine vision; Sieve test; Flow rate; Machine vision; Aggregate; Layer thickness; Sieve test
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2019.06.147

•The watershed algorithm and concave point detection algorithm were used for touching particles.•The effect of the flow rate of aggregates on the accuracy of the sieve test was evaluated.•The effect of the layer thickness for aggregates on the accuracy of the sieve test was estimated.•There is an appropriate layer thickness combination for various specifications of aggregates.•The image segmentation based on machine vision was verified by the road construction projects.
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.