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

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

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

DOI 10.1111/mice.12334

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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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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
ORCID: Cha, Young‐Jin
Choi, Wooram
Mahmoudkhani, Sadegh
Büyüköztürk, Oral
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)
DOI: 10.1111/mice.12334
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 2753 d)
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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Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types

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