The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Pixel-level crack delineation in images with convolutional feature fusion
Open accessAbstract
Cracks in civil structures are important signs of structural degradation and may even indicate the inception of catastrophic failure. Image-based crack detection has been attempted in research communities that bear the potential of replacing human-based inspection. Among many methodologies, deep learning-based cracks detection is actively explored in recent years. However, how to automatically extract cracks quickly and accurately at a pixel level, that is, crack delineation (including both detection and segmentation), is a challenging issue. This article proposes a convolutional neural network-based framework that automates this task through convolutional feature fusion and pixel-level classification. The resulting network architecture with an empirically optimal fusion strategy, termed the crack delineation network, is trained and tested based on a concrete crack image database. The results show that the proposed framework can delineate cracks accurately and rapidly in images towards a fully autonomous machine vision approach to structural crack detection.
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Cites
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Cited by
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
Li, S., Zhao, X., & Zhou, G. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12433 · Cited by 490
SDDNet: Real-Time Crack Segmentation
Choi, W., & Cha, Y.-J. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2945265 · Cited by 462
Dais, D., Bal, İ. E., Smyrou, E., & Sarhosis, V. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103606 · Cited by 429
Structural crack detection using deep convolutional neural networks
Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022) · Automation in Construction · doi:10.1016/j.autcon.2021.103989 · Cited by 369
Metadata
- Authors
- Ni, F., Zhang, J., & Chen, Z.
- Venue
- Structural Control and Health Monitoring
- Publisher
- Wiley
- Published
- 2018-10-31
- Volume
- 26
- Issue
- 1
- Pages
- e2286
- ISSN
- 1545-2255 (ISSN-L)
- OpenAlex
- W2899144041
- Cited by
- 220
- References
- 8
- FWCI
- 16.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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