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Riprap Stockpile Size and Shape Analyses Using Computer Vision
https://orcid.org/http://orcid.org/0000-0003-3237-5328
https://orcid.org/http://orcid.org/0000-0002-2020-8437
https://orcid.org/http://orcid.org/0000-0003-3945-167X
Riprap rocks and large-sized aggregates are being used extensively in geotechnical and hydraulic engineering applications. Knowing the particle size and morphological/shape properties ensures the reliable and sustainable use of aggregate materials at both quarry production lines and construction sites. Traditional methods for assessing aggregates geometric properties involve subjective visual inspection and time-consuming hand measurements, while the use of computer vision analysis techniques is restricted primarily due to the unwieldiness of large-sized aggregates. This paper presents the use of a computer vision system for in-place size and shape evaluation of large aggregates in a stockpile using images of stockpiles conveniently collected using smartphone cameras. The automated image analysis software is comprised of an image segmentation kernel and a morphological analysis module. Comprehensive analyses are conducted on a sample stockpile with multiple view inspections. Based on the findings of this study, the stockpile aggregate image analysis program has great potential as an efficient and innovative application for field-scale and in-place evaluations of aggregate materials.
Riprap Stockpile Size and Shape Analyses Using Computer Vision
https://orcid.org/http://orcid.org/0000-0003-3237-5328
https://orcid.org/http://orcid.org/0000-0002-2020-8437
https://orcid.org/http://orcid.org/0000-0003-3945-167X
Riprap rocks and large-sized aggregates are being used extensively in geotechnical and hydraulic engineering applications. Knowing the particle size and morphological/shape properties ensures the reliable and sustainable use of aggregate materials at both quarry production lines and construction sites. Traditional methods for assessing aggregates geometric properties involve subjective visual inspection and time-consuming hand measurements, while the use of computer vision analysis techniques is restricted primarily due to the unwieldiness of large-sized aggregates. This paper presents the use of a computer vision system for in-place size and shape evaluation of large aggregates in a stockpile using images of stockpiles conveniently collected using smartphone cameras. The automated image analysis software is comprised of an image segmentation kernel and a morphological analysis module. Comprehensive analyses are conducted on a sample stockpile with multiple view inspections. Based on the findings of this study, the stockpile aggregate image analysis program has great potential as an efficient and innovative application for field-scale and in-place evaluations of aggregate materials.
Riprap Stockpile Size and Shape Analyses Using Computer Vision
https://orcid.org/http://orcid.org/0000-0003-3237-5328
https://orcid.org/http://orcid.org/0000-0002-2020-8437
https://orcid.org/http://orcid.org/0000-0003-3945-167X
Riprap rocks and large-sized aggregates are being used extensively in geotechnical and hydraulic engineering applications. Knowing the particle size and morphological/shape properties ensures the reliable and sustainable use of aggregate materials at both quarry production lines and construction sites. Traditional methods for assessing aggregates geometric properties involve subjective visual inspection and time-consuming hand measurements, while the use of computer vision analysis techniques is restricted primarily due to the unwieldiness of large-sized aggregates. This paper presents the use of a computer vision system for in-place size and shape evaluation of large aggregates in a stockpile using images of stockpiles conveniently collected using smartphone cameras. The automated image analysis software is comprised of an image segmentation kernel and a morphological analysis module. Comprehensive analyses are conducted on a sample stockpile with multiple view inspections. Based on the findings of this study, the stockpile aggregate image analysis program has great potential as an efficient and innovative application for field-scale and in-place evaluations of aggregate materials.
Riprap Stockpile Size and Shape Analyses Using Computer Vision
Lecture Notes in Civil Engineering
Tutumluer, Erol (editor) / Nazarian, Soheil (editor) / Al-Qadi, Imad (editor) / Qamhia, Issam I.A. (editor) / Luo, Jiayi (author) / Huang, Haohang (author) / Qamhia, Issam (author) / Hart, John M. (author) / Tutumluer, Erol (author)
2021-08-05
11 pages
Article/Chapter (Book)
Electronic Resource
English
Aggregate stockpile , Particle size and shape , Morphological analyses , Computer vision , Object detection , Image segmentation , Deep learning Engineering , Geoengineering, Foundations, Hydraulics , Geotechnical Engineering & Applied Earth Sciences , Transportation Technology and Traffic Engineering
Riprap Stockpile Size and Shape Analyses Using Computer Vision
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