Details
| Original language | English |
|---|---|
| Pages (from-to) | 443-450 |
| Number of pages | 8 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 10 |
| Issue number | 4/W1-2022 |
| Publication status | Published - 14 Jan 2023 |
| Event | 6th SMPR and 4th GIResearch, ISPRS Geospatial Conference - Duration: 19 Feb 2023 → 22 Feb 2023 |
Abstract
Keywords
- Crack Segmentation, Crack 3D Reconstruction, DeepLabv3+, Xception, Structural Health Monitoring, Deep Learning, Structure From Motion, Civil Infrastructures
ASJC Scopus subject areas
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 10, No. 4/W1-2022, 14.01.2023, p. 443-450.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Intelligent 3D Crack Reconstruction using Close Range Photogrammetry Imagery
AU - Majidi, Soheil
AU - Omidalizarandi, Mohammad
AU - Sharifi, M.A.
PY - 2023/1/14
Y1 - 2023/1/14
N2 - Civil infrastructure structural health monitoring (SHM) and its preservation from deterioration is a crucial task. In general, natural disasters like severe earthquakes, extreme landslides, subsidence or intensive floods directly influence the health of civil structures such as buildings, bridges, roads and dams. Evaluation and inspection of defects and damages of the aforementioned structures help to preserve them from destruction by accelerating rehabilitation and reconstruction. An automatic and precise crack detection framework is required for periodic assessment and inspection due to the large number of the structures. In this study, a two-step crack segmentation and its 3D reconstruction procedure is proposed. The crack segmentation is carried out by using Deeplabv3+ architecture and Xception as the backbone. Next, Squeeze-and-Excitation is added as an attention module to achieve higher accuracy. Integration of predicted masks and original images into a structure-from-motion procedure is additionally taken into account. In the last step, ground control points and scale bars are considered to overcome the problem of datum rank deficiency in absolute orientation through the bundle adjustment procedure in aerial triangulation. The most probable segmented cracks are overlaid on the 3D point clouds in the global coordinate system with true scale. Our network is trained based on 8000 images and their corresponding masks, leading to 69% in Intersection over Union (IoU) index. Submillimeter accuracy of crack reconstruction using the proposed methodology is validated with a scale bar.
AB - Civil infrastructure structural health monitoring (SHM) and its preservation from deterioration is a crucial task. In general, natural disasters like severe earthquakes, extreme landslides, subsidence or intensive floods directly influence the health of civil structures such as buildings, bridges, roads and dams. Evaluation and inspection of defects and damages of the aforementioned structures help to preserve them from destruction by accelerating rehabilitation and reconstruction. An automatic and precise crack detection framework is required for periodic assessment and inspection due to the large number of the structures. In this study, a two-step crack segmentation and its 3D reconstruction procedure is proposed. The crack segmentation is carried out by using Deeplabv3+ architecture and Xception as the backbone. Next, Squeeze-and-Excitation is added as an attention module to achieve higher accuracy. Integration of predicted masks and original images into a structure-from-motion procedure is additionally taken into account. In the last step, ground control points and scale bars are considered to overcome the problem of datum rank deficiency in absolute orientation through the bundle adjustment procedure in aerial triangulation. The most probable segmented cracks are overlaid on the 3D point clouds in the global coordinate system with true scale. Our network is trained based on 8000 images and their corresponding masks, leading to 69% in Intersection over Union (IoU) index. Submillimeter accuracy of crack reconstruction using the proposed methodology is validated with a scale bar.
KW - Crack Segmentation
KW - Crack 3D Reconstruction
KW - DeepLabv3+
KW - Xception
KW - Structural Health Monitoring
KW - Deep Learning
KW - Structure From Motion
KW - Civil Infrastructures
UR - http://www.scopus.com/inward/record.url?scp=85146923387&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-X-4-W1-2022-443-2023
DO - 10.5194/isprs-annals-X-4-W1-2022-443-2023
M3 - Conference article
VL - 10
SP - 443
EP - 450
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 4/W1-2022
T2 - 6th SMPR and 4th GIResearch, ISPRS Geospatial Conference
Y2 - 19 February 2023 through 22 February 2023
ER -