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

Haohang Huang

TL;DR

This dissertation addresses major challenges of morphological characterization of aggregates as a multi-scenario solution by developing a field imaging framework for the morphological characterization of aggregates as a multi-scenario solution.

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.

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

TL;DR

This dissertation addresses major challenges of morphological characterization of aggregates as a multi-scenario solution by developing a field imaging framework for the morphological characterization of aggregates as a multi-scenario solution.

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.
Paper Structure (137 sections, 35 equations, 110 figures, 23 tables)

This paper contains 137 sections, 35 equations, 110 figures, 23 tables.

Table of Contents

  1. Introduction
  2. Research Statement
  3. Research Objectives
  4. Research Methodology and Scope
  5. Dissertation Outline
  6. Background Literature Review
  7. Construction Aggregates Industry
  8. Aggregate Production and Manufacturing Process
  9. Types of Aggregates and Their Engineering Applications
  10. Fine Aggregates and Coarse Aggregates
  11. Riprap Material and Large-Sized Aggregates
  12. State-of-the-Practice Characterization Methods for Regular-Sized and Large-Sized Aggregates
  13. Laboratory Methods for Regular-sized Aggregates
  14. Field Methods for Large-sized Aggregates
  15. State-of-the-Art Aggregates Characterization Based on Machine Vision
  16. ...and 122 more sections

Figures (110)

  • Figure 1: Schematic outline of the dissertation
  • Figure 2: Natural aggregate consumption in the United States (historical and projected) after kelly_crushed_1998
  • Figure 3: Value of Non-fuel Minerals Produced in 2020 summaries_mineral_2021
  • Figure 4: Value of Crushed Stone Produced in 2020 summaries_mineral_2021
  • Figure 5: Value of Construction Sand and Gravel Produced in 2020 summaries_mineral_2021
  • ...and 105 more figures