The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Analysis of Edge-Detection Techniques for Crack Identification in Bridges
Abstract
Bridge monitoring and maintenance is an expensive yet essential task in maintaining a safe national transportation infrastructure. Traditional monitoring methods use visual inspection of bridges on a regular basis and often require inspectors to travel to the bridge of concern and determine the deterioration level of the bridge. Automation of this process may result in great monetary savings and can lead to more frequent inspection cycles. One aspect of this automation is the detection of cracks and deterioration of a bridge. This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny. These imaging edge-detection algorithms were implemented in MatLab and simulated using a sample of 50 concrete bridge images (25 with cracks and 25 without). The results show that the FHT was significantly more reliable than the other three edge-detection techniques in identifying cracks.
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Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
An algorithm for the machine calculation of complex Fourier series
Cooley, J. W., & Tukey, J. W. (1965) · Mathematics of Computation · doi:10.1090/s0025-5718-1965-0178586-1 · Cited by 13,154
Algorithms for image processing and computer vision
Parker, J. R. (1996) · Cited by 1,501
Primitive‐Based Classification of Pavement Cracking Images
Koutsopoulos, H. N., & Downey, A. B. (1993) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(1993)119:3(402 · Cited by 96
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
Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015) · Advanced Engineering Informatics · doi:10.1016/j.aei.2015.01.008 · Cited by 1,065
Chen, F.-C., & Jahanshahi, M. R. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2764844 · Cited by 930
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
Metadata
- Authors
- Abdel-Qader, I., Abudayyeh, O., & Kelly, M. E.
- Venue
- Journal of Computing in Civil Engineering
- Published
- 2003-10
- Volume
- 17
- Issue
- 4
- Pages
- 255-263
- ISSN
- 0887-3801 (ISSN-L)
- 1943-5487
- DOI
- 10.1061/(asce)0887-3801(2003)17:4(255
- OpenAlex
- W2005029343
- Cited by
- 892
- References
- 4
- FWCI
- 16.04
- Type
- journal article
- Language
- en
- Open access
- closed
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- Abdel-Qader, I., Abudayyeh, O., & Kelly, M. E. (2003). Analysis of Edge-Detection Techniques for Crack Identification in Bridges. Journal of Computing in Civil Engineering. https://doi.org/10.1061/(asce)0887-3801(2003)17%3A4(255
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