Data augmentation-assisted muck image recognition during shield tunnelling Article Swipe
Tao Yan
,
Shui‐Long Shen
,
Annan Zhou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.undsp.2024.10.001
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.undsp.2024.10.001
This paper proposed a framework for muck types identification based on data augmentation-assisted image recognition during shield tunnelling. The muck pictures were collected from the shield monitoring system above the conveyor belt. The data augmentation operations were then used to increase the quality of the original images. Furthermore, the Bayesian optimisation algorithm was employed to adjust the parameters of augmenters and highlight the features of the photos. The deep image recognition algorithms (AlexNet and GoogLeNet) were trained and enhanced by the augmentation images, which were used to establish the muck types identification models and assessed by the evaluation indices. Model efficiency was analysed through the performance and time cost of training and validation processes to select the optimal model for muck types identification. Results showed that the performance of identification models could be highly increased by data augmentation with Bayesian optimisation, and the enhanced GoogLeNet performed the highest efficiency for muck types identification.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.undsp.2024.10.001
- OA Status
- diamond
- Cited By
- 7
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404993957
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404993957Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.undsp.2024.10.001Digital Object Identifier
- Title
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Data augmentation-assisted muck image recognition during shield tunnellingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-04Full publication date if available
- Authors
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Tao Yan, Shui‐Long Shen, Annan ZhouList of authors in order
- Landing page
-
https://doi.org/10.1016/j.undsp.2024.10.001Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.undsp.2024.10.001Direct OA link when available
- Concepts
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Muck, Shield, Quantum tunnelling, Geology, Geotechnical engineering, Mining engineering, Forensic engineering, Engineering, Soil science, Materials science, Petrology, OptoelectronicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.8855176 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |