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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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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
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Research works printed but not linked: 2
- Park, J., Kim, T. & Kim, J. (2015), Image‐based bolt‐loosening detection technique of bolt joint in steel bridges, in Proceedings of the 6th International Conference on Advances in Experimental Structural Engineering (6AESE), Urbana–Champaign, IL, 1–2 August 2015.
- Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R. & Lecun, Y. (2014), Overfeat: integrated recognition, localization and detection using convolutional networks, in Proceedings of the International Conference on Learning Representations (ICLR2014), Banff, Canada, 14–16 April 2014.
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Spencer, B. F., Hoskere, V., & Narazaki, Y. (2019) · Engineering · doi:10.1016/j.eng.2018.11.030 · Cited by 1,273
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Autonomous concrete crack detection using deep fully convolutional neural network
Dung, C. V., & Anh, L. D. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.11.028 · Cited by 1,170
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Dorafshan, S., Thomas, R. J., & Maguire, M. (2018) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2018.08.011 · Cited by 821
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Gao, Y., & Mosalam, K. M. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12363 · Cited by 787
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Sun, L., Shang, Z., Xia, Y., Bhowmick, S., & Nagarajaiah, S. (2020) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002535 · Cited by 751
Machine Learning for Structural Engineering: A State-of-the-Art Review
Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748
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Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103081 · Cited by 679
Automatic classification of defective photovoltaic module cells in electroluminescence images
Deitsch, S., Christlein, V., Berger, S., Buerhop‐Lutz, C., Maier, A., Gallwitz, F., & Rieß, C. (2019) · Solar Energy · doi:10.1016/j.solener.2019.02.067 · Cited by 607
Sun, H., Burton, H. V., & Huang, H. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101816 · Cited by 576
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Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559
Li, S., Zhao, X., & Zhou, G. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12433 · Cited by 500
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Tang, Z., Chen, Z., Bao, Y., & Li, H. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2296 · Cited by 445
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Kang, D., Benipal, S. S., Gopal, D. L., & Cha, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103291 · Cited by 424
Self-citationA systematic review of convolutional neural network-based structural condition assessment techniques
Sony, S., Dunphy, K., Sadhu, A., & Capretz, M. A. M. (2020) · Engineering Structures · doi:10.1016/j.engstruct.2020.111347 · Cited by 415
Deep learning-based road damage detection and classification for multiple countries
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Liang, X. P. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12425 · Cited by 406
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Zhao, C., Shu, X., Yan, X., Zuo, X., & Zhu, F. (2023) · Measurement · doi:10.1016/j.measurement.2023.112776 · Cited by 409
Huang, M., Ninić, J., & Zhang, Q. (2020) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2020.103677 · Cited by 412
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Lei, X., Sun, L., & Xia, Y. (2021) · Structural Health Monitoring · doi:10.1177/1475921720959226 · Cited by 236
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Chen, J., Dan, H.-C., Ding, Y., Gao, Y., Guo, M., Guo, S., Han, B., Hong, B., Hou, Y., Hu, C., et al. (2021) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2021.10.001 · Cited by 189
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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
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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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