The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Vision‐Based Automated Crack Detection for Bridge Inspection
Open accessAbstract
Visual inspection of bridges is customarily used to identify and evaluate faults. However, current procedures followed by human inspectors demand long inspection times to examine large and difficult to access bridges. Also, highly relying on an inspector's subjective or empirical knowledge induces false evaluation. To address these limitations, a vision‐based visual inspection technique is proposed by automatically processing and analyzing a large volume of collected images. Images used in this technique are captured without controlling angles and positions of cameras and no need for preliminary calibration. As a pilot study, cracks near bolts on a steel structure are identified from images. Using images from many different angles and prior knowledge of the typical appearance and characteristics of this class of faults, the proposed technique can successfully detect cracks near bolts.
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Cites
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Cited by
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12334 · Cited by 1,558
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network
Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., & Yang, X. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12412 · Cited by 852
Computer vision-based concrete crack detection using U-net fully convolutional networks
Liu, Z., Cao, Y., Wang, Y., & Wang, W. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.04.005 · Cited by 833
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Metadata
- Authors
- Yeum, C. M., & Dyke, S. J.
- Venue
- Computer-Aided Civil and Infrastructure Engineering
- Publisher
- Elsevier BV
- Published
- 2015-05-15
- Volume
- 30
- Issue
- 10
- Pages
- 759-770
- ISSN
- 1093-9687 (ISSN-L)
- 1467-8667
- OpenAlex
- W2126052502
- Cited by
- 517
- References
- 29
- FWCI
- 42.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
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