Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network Article Swipe
Chuan‐Zhi Dong
,
Liangding Li
,
Jin Yan
,
Zhiming Zhang
,
Hong Pan
,
F. Necati Çatbaş
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21124135
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21124135
Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segmentation can support condition assessment, asset maintenance, and management of existing structures and prevent the early permit post and improve life cycles. In current research and engineering practices, visual inspection is the most widely implemented approach for fatigue crack inspection. However, the inspection accuracy of this method highly relies on the subjective judgment of the inspectors. Furthermore, it needs large amounts of cost, time, and labor force. Non-destructive testing methods can provide accurate detection results, but the cost is very high. To overcome the limitations of current fatigue crack detection methods, this study presents a pixel-level fatigue crack segmentation framework for large-scale images with complicated backgrounds taken from steel structures by using an encoder-decoder network, which is modified from the U-net structure. To effectively train and test the images with large resolutions such as 4928 × 3264 pixels or larger, the large images were cropped into small images for training and testing. The final segmentation results of the original images are obtained by assembling the segment results in the small images. Additionally, image post-processing including opening and closing operations were implemented to reduce the noises in the segmentation maps. The proposed method achieved an acceptable accuracy of automatic fatigue crack segmentation in terms of average intersection over union (mIOU). A comparative study with an FCN model that implements ResNet34 as backbone indicates that the proposed method using U-net could give better fatigue crack segmentation performance with fewer training epochs and simpler model structure. Furthermore, this study also provides helpful considerations and recommendations for researchers and practitioners in civil infrastructure engineering to apply image-based fatigue crack detection.
Related Topics
Concepts
Segmentation
Pixel
Encoder
Computer science
Intersection (aeronautics)
Artificial intelligence
Image segmentation
Distortion (music)
Noise (video)
Structural engineering
Engineering
Computer vision
Image (mathematics)
Aerospace engineering
Bandwidth (computing)
Computer network
Amplifier
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21124135
- https://www.mdpi.com/1424-8220/21/12/4135/pdf?version=1623848225
- OA Status
- gold
- Cited By
- 39
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3166170336
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3166170336Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21124135Digital Object Identifier
- Title
-
Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-16Full publication date if available
- Authors
-
Chuan‐Zhi Dong, Liangding Li, Jin Yan, Zhiming Zhang, Hong Pan, F. Necati ÇatbaşList of authors in order
- Landing page
-
https://doi.org/10.3390/s21124135Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/12/4135/pdf?version=1623848225Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/21/12/4135/pdf?version=1623848225Direct OA link when available
- Concepts
-
Segmentation, Pixel, Encoder, Computer science, Intersection (aeronautics), Artificial intelligence, Image segmentation, Distortion (music), Noise (video), Structural engineering, Engineering, Computer vision, Image (mathematics), Aerospace engineering, Bandwidth (computing), Computer network, Amplifier, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
39Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 10, 2023: 9, 2022: 7, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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