University of Illinois Urbana-Champaign

3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2

Haohang Huang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John M. Hart

Content Files
FHWA-ICT-22-013.pdf

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https://hdl.handle.net/2142/114476

Description

Title
3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2
Author(s)
Haohang Huang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John M. Hart
Issue Date
2022-09
Keyword(s)
  • Riprap
  • Aggregate Stockpile
  • Field Imaging
  • Computer Vision
  • Deep Learning
  • 3D Reconstruction
  • 3D Segmentation
  • Size and Shape Analyses
Abstract
Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry production process and at construction sites, particularly for aggregates in stockpile form. This research study presents a 3D reconstruction-segmentation-completion approach based on deep learning techniques by combining three developed research components: field 3D reconstruction procedures, 3D stockpile instance segmentation, and 3D shape completion. The approach was designed to reconstruct aggregate stockpiles from multi-view images, segment the stockpile into individual instances, and predict the unseen side of each instance (particle) based on the partial visible shapes. Based on the dataset constructed from individual aggregate models, a state-of-the-art 3D instance segmentation network and a 3D shape completion network were implemented and trained, respectively. The application of the integrated approach was demonstrated on re-engineered stockpiles and field stockpiles. The validation of results using ground-truth measurements showed satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. The algorithms are integrated into a software application with a user-friendly graphical user interface. Based on the findings of this study, this stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site QA/QC tasks of riprap rock and aggregate stockpiles.
Publisher
Illinois Center for Transportation/Illinois Department of Transportation
Series/Report Name or Number
FHWA-ICT-22-013
Type of Resource
text
Genre of Resource
technical report
Language
en
Handle URL
https://hdl.handle.net/2142/114476
Sponsor(s)/Grant Number(s)
IDOT-R27-214
Copyright and License Information
No restrictions. This document is available through the National Technical Information Service, Springfield, VA 22161.

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Research and Publications - Illinois Center for Transportation PRIMARY