Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training Strategy Article Swipe
Shuyuan Zhang
,
Hongli Xu
,
Xiaoran Zhu
,
Lipeng Xie
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2478/fcds-2024-0007
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2478/fcds-2024-0007
Automatic crack detection in construction facilities is a challenging yet crucial task. However, existing deep learning (DL)-based semantic segmentation methods for this field are based on fully supervised learning models and pixel-level manual annotation, which are time-consuming and labor-intensive. To solve this problem, this paper proposes a novel crack semantic segmentation network using weakly supervised approach and mixed-label training strategy. Firstly, an image patch-level classifier of crack is trained to generate a coarse localization map for automatic pseudo-labeling of cracks combined with a thresholding-based method. Then, we integrated the pseudo-annotated with manual-annotated samples with a ratio of 4:1 to train the crack segmentation network with a mixed-label training strategy, in which the manual labels were assigned with a higher weight value. The experimental data on two public datasets demonstrate that our proposed method achieves a comparable accuracy with the fully supervised methods, reducing over 65% of the manual annotation workload.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2478/fcds-2024-0007
- OA Status
- diamond
- Cited By
- 1
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391909911
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391909911Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2478/fcds-2024-0007Digital Object Identifier
- Title
-
Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training StrategyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-01Full publication date if available
- Authors
-
Shuyuan Zhang, Hongli Xu, Xiaoran Zhu, Lipeng XieList of authors in order
- Landing page
-
https://doi.org/10.2478/fcds-2024-0007Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.2478/fcds-2024-0007Direct OA link when available
- Concepts
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Computer science, Segmentation, Artificial intelligence, Thresholding, Annotation, Classifier (UML), Workload, Machine learning, Supervised learning, Pattern recognition (psychology), Artificial neural network, Image (mathematics), Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.4:1 | 98 |
| abstract_inverted_index.65% | 145 |
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| abstract_inverted_index.the | 89, 101, 112, 139, 147 |
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| abstract_inverted_index.yet | 10 |
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| abstract_inverted_index.deep | 15 |
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| abstract_inverted_index.this | 22, 42, 44 |
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| abstract_inverted_index.Then, | 86 |
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| abstract_inverted_index.crack | 2, 49, 67, 102 |
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| abstract_inverted_index.fully | 27, 140 |
| abstract_inverted_index.image | 63 |
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| abstract_inverted_index.paper | 45 |
| abstract_inverted_index.ratio | 96 |
| abstract_inverted_index.solve | 41 |
| abstract_inverted_index.task. | 12 |
| abstract_inverted_index.train | 100 |
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| abstract_inverted_index.coarse | 73 |
| abstract_inverted_index.cracks | 80 |
| abstract_inverted_index.higher | 119 |
| abstract_inverted_index.labels | 114 |
| abstract_inverted_index.manual | 33, 113, 148 |
| abstract_inverted_index.method | 133 |
| abstract_inverted_index.models | 30 |
| abstract_inverted_index.public | 127 |
| abstract_inverted_index.value. | 121 |
| abstract_inverted_index.weakly | 54 |
| abstract_inverted_index.weight | 120 |
| abstract_inverted_index.crucial | 11 |
| abstract_inverted_index.method. | 85 |
| abstract_inverted_index.methods | 20 |
| abstract_inverted_index.network | 52, 104 |
| abstract_inverted_index.samples | 93 |
| abstract_inverted_index.trained | 69 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Firstly, | 61 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.accuracy | 137 |
| abstract_inverted_index.achieves | 134 |
| abstract_inverted_index.approach | 56 |
| abstract_inverted_index.assigned | 116 |
| abstract_inverted_index.combined | 81 |
| abstract_inverted_index.datasets | 128 |
| abstract_inverted_index.existing | 14 |
| abstract_inverted_index.generate | 71 |
| abstract_inverted_index.learning | 16, 29 |
| abstract_inverted_index.methods, | 142 |
| abstract_inverted_index.problem, | 43 |
| abstract_inverted_index.proposed | 132 |
| abstract_inverted_index.proposes | 46 |
| abstract_inverted_index.reducing | 143 |
| abstract_inverted_index.semantic | 18, 50 |
| abstract_inverted_index.training | 59, 108 |
| abstract_inverted_index.Automatic | 1 |
| abstract_inverted_index.automatic | 77 |
| abstract_inverted_index.detection | 3 |
| abstract_inverted_index.strategy, | 109 |
| abstract_inverted_index.strategy. | 60 |
| abstract_inverted_index.workload. | 150 |
| abstract_inverted_index.(DL)-based | 17 |
| abstract_inverted_index.annotation | 149 |
| abstract_inverted_index.classifier | 65 |
| abstract_inverted_index.comparable | 136 |
| abstract_inverted_index.facilities | 6 |
| abstract_inverted_index.integrated | 88 |
| abstract_inverted_index.supervised | 28, 55, 141 |
| abstract_inverted_index.annotation, | 34 |
| abstract_inverted_index.challenging | 9 |
| abstract_inverted_index.demonstrate | 129 |
| abstract_inverted_index.mixed-label | 58, 107 |
| abstract_inverted_index.patch-level | 64 |
| abstract_inverted_index.pixel-level | 32 |
| abstract_inverted_index.construction | 5 |
| abstract_inverted_index.experimental | 123 |
| abstract_inverted_index.localization | 74 |
| abstract_inverted_index.segmentation | 19, 51, 103 |
| abstract_inverted_index.time-consuming | 37 |
| abstract_inverted_index.pseudo-labeling | 78 |
| abstract_inverted_index.labor-intensive. | 39 |
| abstract_inverted_index.manual-annotated | 92 |
| abstract_inverted_index.pseudo-annotated | 90 |
| abstract_inverted_index.thresholding-based | 84 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.53048358 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |