Cited by 1,558Infrastructure Maintenance and MonitoringStructural Health Monitoring TechniquesIndustrial Vision Systems and Defect Detection

Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types

Open access
Cha, Young‐Jin, Choi, Wooram, Suh, Gahyun, Mahmoudkhani, Sadegh, Büyüköztürk, Oral·2018·Computer-Aided Civil and Infrastructure Engineering

Abstract

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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Crossref · 10.1111/mice.12334

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38 of 38 references verified0 citation errors · 0 not checkable

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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
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

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