The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types
Open accessAbstract
Computer vision-based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real-time simultaneous detection of multiple types of damages, a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R-CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures. Its performance is also compared to that of the traditional CNN-based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real-time damage detection on video using the trained networks is developed.
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Metadata
- Authors
- Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O.
- Affiliations
- University of Manitoba (CA)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Cha, Young‐Jin
- Venue
- Computer-Aided Civil and Infrastructure Engineering
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 33
- Issue
- 9
- Pages
- 731-747
- ISSN
- 1093-9687 (ISSN-L)
- OpenAlex
- W2768955070
- Cited by
- 1,558
- References
- 38
- Citations by year
- 2026: 107 · 2025: 205 · 2024: 264 · 2023: 264 · 2022: 231 · 2021: 186 · 2020: 160 · 2019: 98 · 2018: 43
- FWCI
- 86.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computer-Aided Civil and Infrastructure Engineering (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- damage detection, Faster R-CNN, region-based object detection, concrete cracking, steel corrosion, bolt corrosion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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