The DOI matches its Crossref record.
Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform
Abstract
This paper presents a new approach in automation for crack detection on pavement surface images. The method is based on the continuous wavelet transform. In the first step, a separable 2D continuous wavelet transform for several scales is performed. Complex coefficient maps are built. The angle and modulus information are used to keep significant coefficients. Then, wavelet coefficients maximal values are searched and their propagation through scales is analyzed. Finally, a post-processing gives a binary image which indicates the presence or not of cracks on the pavement surface image.
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Cited by
Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review
Gopalakrishnan, K. (2018) · Data · doi:10.3390/data3030028 · Cited by 210
Metadata
- Authors
- Subirats, P., Dumoulin, J., Legeay, V., & Barba, D.
- Venue
- 2006 International Conference on Image Processing
- Publisher
- IEEE
- Published
- 2006-10
- Pages
- 3037-3040
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W1969393996
- Cited by
- 260
- References
- 0
- FWCI
- 7.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform. https://app.cochat.ai/papers/10.1109/icip.2006.313007
- The paper
- Subirats, P., Dumoulin, J., Legeay, V., & Barba, D. (2006). Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform. 2006 International Conference on Image Processing. https://doi.org/10.1109/icip.2006.313007
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
