The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Machine Learning for Crack Detection: Review and Model Performance Comparison
Abstract
With the advancement of machine learning (ML) and deep learning (DL), there is a great opportunity to enhance the development of automatic crack detection algorithms. In this paper, the authors organize and provide up-to-date information on on ML-based crack detection algorithms for researchers to more efficiently seek potential focus and direction. The authors first reviewed 68 ML-based crack detection methods to identify the current trend of development, pixel-level crack segmentation. The authors then conducted a performance evaluation on 8 ML-based crack segmentation models using consistent evaluation metrics and three-dimensional (3D) pavement images with diverse conditions to identify remaining challenges and potential directions for future development. Based on the comparison results, deeper backbone networks in FCN models and skip connections in U-Net both improved the performance. Within different categories of pavement images, except for the Other Distress category, FCN and U-Net scored over 90 on the enhanced Hausdorff distance metric. Results showed that solving the false-positive problem is an important step in further improving ML-based crack detection models.
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Cites
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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
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Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
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Image-to-Image Translation with Conditional Adversarial Networks
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SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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Conditional Generative Adversarial Nets
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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
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Hamishebahar, Y., Guan, H., So, S., & Jo, J. (2022) · Applied Sciences · doi:10.3390/app12031374 · Cited by 188
Metadata
- Authors
- Hsieh, Y.-A., & Tsai, Y. J.
- Venue
- Journal of Computing in Civil Engineering
- Published
- 2020-09
- Volume
- 34
- Issue
- 5
- ISSN
- 0887-3801 (ISSN-L)
- 1943-5487
- OpenAlex
- W3040786507
- Cited by
- 420
- References
- 81
- FWCI
- 24.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hsieh, Y.-A., & Tsai, Y. J. (2020). Machine Learning for Crack Detection: Review and Model Performance Comparison. Journal of Computing in Civil Engineering. https://doi.org/10.1061/(asce)cp.1943-5487.0000918
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