Field imaging framework for morphological characterization of aggregates with computer vision: Algorithms and applications

TL;DR

This paper develops a comprehensive field imaging framework using computer vision for morphological analysis of aggregates, addressing limitations of existing methods and enabling multi-scenario applications in construction.

Contribution

It introduces novel algorithms and a unified framework for 2D and 3D aggregate analysis in real-world conditions, including 3D reconstruction, segmentation, and shape completion.

Findings

High-fidelity 3D models of aggregates were successfully reconstructed.

The integrated approach accurately segmented and predicted unseen aggregate sides.

The framework demonstrated effective analysis on real stockpiles with validation against ground-truth.

Abstract

Construction aggregates, including sand and gravel, crushed stone and riprap, are the core building blocks of the construction industry. State-of-the-practice characterization methods mainly relies on visual inspection and manual measurement. State-of-the-art aggregate imaging methods have limitations that are only applicable to regular-sized aggregates under well-controlled conditions. This dissertation addresses these major challenges by developing a field imaging framework for the morphological characterization of aggregates as a multi-scenario solution. For individual and non-overlapping aggregates, a field imaging system was designed and the associated segmentation and volume estimation algorithms were developed. For 2D image analyses of aggregates in stockpiles, an automated 2D instance segmentation and morphological analysis approach was established. For 3D point cloud analyses of aggregate stockpiles, an integrated 3D Reconstruction-Segmentation-Completion (RSC-3D) approach was established: 3D reconstruction procedures from multi-view images, 3D stockpile instance segmentation, and 3D shape completion to predict the unseen sides. First, a 3D reconstruction procedure was developed to obtain high-fidelity 3D models of collected aggregate samples, based on which a 3D aggregate particle library was constructed. Next, two datasets were derived from the 3D particle library for 3D learning: a synthetic dataset of aggregate stockpiles with ground-truth instance labels, and a dataset of partial-complete shape pairs, developed with varying-view raycasting schemes. A state-of-the-art 3D instance segmentation network and a 3D shape completion network were trained on the datasets, respectively. The application of the integrated approach was demonstrated on real stockpiles and validated with ground-truth, showing good performance in capturing and predicting the unseen sides of aggregates.