Pixel-Level Crack Detection and Quantification of Nuclear Containment with Deep Learning Article Swipe
Jian Yu
,
Yaming Xu
,
Xing Cheng
,
Jianguo Zhou
,
Pai Pan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/9982080
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/9982080
Crack detection based on deep learning is an advanced technology, and many scholars have proposed many methods for the segmentation of pavement cracks. However, due to the difference of image specifications and crack characteristics, some existing methods are not effective in detecting cracks of containment. To quickly detect cracks and accurately extract crack quantitative information, this paper proposes a crack detection model, called MA_CrackNet, based on deep learning and a crack quantitative analysis algorithm. MA_CrackNet is an end-to-end model based on multiscale fusions that achieve pixel-level segmentation of cracks. Experimental results show that the proposed MA_CrackNet has excellent performance in the crack detection task of nuclear containment, achieving a precision, recall, F1, and mean intersection-over-union (mIoU) of 86.07%, 89.96%, 87.97%, and 89.19%, respectively, outperforming other advanced semantic segmentation models. The quantification algorithm automatically measures the four characteristic indicators of the crack, namely, the length of the crack, the area, the maximum width, and the mean width and obtains reliable results.
Related Topics
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2023/9982080
- https://downloads.hindawi.com/journals/schm/2023/9982080.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385727812
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385727812Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2023/9982080Digital Object Identifier
- Title
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Pixel-Level Crack Detection and Quantification of Nuclear Containment with Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-10Full publication date if available
- Authors
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Jian Yu, Yaming Xu, Xing Cheng, Jianguo Zhou, Pai PanList of authors in order
- Landing page
-
https://doi.org/10.1155/2023/9982080Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/schm/2023/9982080.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/schm/2023/9982080.pdfDirect OA link when available
- Concepts
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Intersection (aeronautics), Segmentation, Containment (computer programming), Pixel, Computer science, Artificial intelligence, Deep learning, Pattern recognition (psychology), Structural engineering, Engineering, Aerospace engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 3, 2024: 3Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 20, 28, 43, 87, 104, 116, 138, 144 |
| abstract_inverted_index.on | 3, 65, 80 |
| abstract_inverted_index.to | 25 |
| abstract_inverted_index.F1, | 111 |
| abstract_inverted_index.The | 129 |
| abstract_inverted_index.and | 10, 31, 49, 68, 112, 120, 152, 156 |
| abstract_inverted_index.are | 37 |
| abstract_inverted_index.due | 24 |
| abstract_inverted_index.for | 17 |
| abstract_inverted_index.has | 96 |
| abstract_inverted_index.not | 38 |
| abstract_inverted_index.the | 18, 26, 93, 100, 134, 139, 142, 145, 147, 149, 153 |
| abstract_inverted_index.deep | 4, 66 |
| abstract_inverted_index.four | 135 |
| abstract_inverted_index.have | 13 |
| abstract_inverted_index.many | 11, 15 |
| abstract_inverted_index.mean | 113, 154 |
| abstract_inverted_index.show | 91 |
| abstract_inverted_index.some | 34 |
| abstract_inverted_index.task | 103 |
| abstract_inverted_index.that | 83, 92 |
| abstract_inverted_index.this | 55 |
| abstract_inverted_index.Crack | 0 |
| abstract_inverted_index.area, | 148 |
| abstract_inverted_index.based | 2, 64, 79 |
| abstract_inverted_index.crack | 32, 52, 59, 70, 101 |
| abstract_inverted_index.image | 29 |
| abstract_inverted_index.model | 78 |
| abstract_inverted_index.other | 124 |
| abstract_inverted_index.paper | 56 |
| abstract_inverted_index.width | 155 |
| abstract_inverted_index.(mIoU) | 115 |
| abstract_inverted_index.called | 62 |
| abstract_inverted_index.crack, | 140, 146 |
| abstract_inverted_index.cracks | 42, 48 |
| abstract_inverted_index.detect | 47 |
| abstract_inverted_index.length | 143 |
| abstract_inverted_index.model, | 61 |
| abstract_inverted_index.width, | 151 |
| abstract_inverted_index.86.07%, | 117 |
| abstract_inverted_index.87.97%, | 119 |
| abstract_inverted_index.89.19%, | 121 |
| abstract_inverted_index.89.96%, | 118 |
| abstract_inverted_index.achieve | 84 |
| abstract_inverted_index.cracks. | 22, 88 |
| abstract_inverted_index.extract | 51 |
| abstract_inverted_index.fusions | 82 |
| abstract_inverted_index.maximum | 150 |
| abstract_inverted_index.methods | 16, 36 |
| abstract_inverted_index.models. | 128 |
| abstract_inverted_index.namely, | 141 |
| abstract_inverted_index.nuclear | 105 |
| abstract_inverted_index.obtains | 157 |
| abstract_inverted_index.quickly | 46 |
| abstract_inverted_index.recall, | 110 |
| abstract_inverted_index.results | 90 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.advanced | 8, 125 |
| abstract_inverted_index.analysis | 72 |
| abstract_inverted_index.existing | 35 |
| abstract_inverted_index.learning | 5, 67 |
| abstract_inverted_index.measures | 133 |
| abstract_inverted_index.pavement | 21 |
| abstract_inverted_index.proposed | 14, 94 |
| abstract_inverted_index.proposes | 57 |
| abstract_inverted_index.reliable | 158 |
| abstract_inverted_index.results. | 159 |
| abstract_inverted_index.scholars | 12 |
| abstract_inverted_index.semantic | 126 |
| abstract_inverted_index.achieving | 107 |
| abstract_inverted_index.algorithm | 131 |
| abstract_inverted_index.detecting | 41 |
| abstract_inverted_index.detection | 1, 60, 102 |
| abstract_inverted_index.effective | 39 |
| abstract_inverted_index.excellent | 97 |
| abstract_inverted_index.accurately | 50 |
| abstract_inverted_index.algorithm. | 73 |
| abstract_inverted_index.difference | 27 |
| abstract_inverted_index.end-to-end | 77 |
| abstract_inverted_index.indicators | 137 |
| abstract_inverted_index.multiscale | 81 |
| abstract_inverted_index.precision, | 109 |
| abstract_inverted_index.MA_CrackNet | 74, 95 |
| abstract_inverted_index.performance | 98 |
| abstract_inverted_index.pixel-level | 85 |
| abstract_inverted_index.technology, | 9 |
| abstract_inverted_index.Experimental | 89 |
| abstract_inverted_index.MA_CrackNet, | 63 |
| abstract_inverted_index.containment, | 106 |
| abstract_inverted_index.containment. | 44 |
| abstract_inverted_index.information, | 54 |
| abstract_inverted_index.quantitative | 53, 71 |
| abstract_inverted_index.segmentation | 19, 86, 127 |
| abstract_inverted_index.automatically | 132 |
| abstract_inverted_index.outperforming | 123 |
| abstract_inverted_index.respectively, | 122 |
| abstract_inverted_index.characteristic | 136 |
| abstract_inverted_index.quantification | 130 |
| abstract_inverted_index.specifications | 30 |
| abstract_inverted_index.characteristics, | 33 |
| abstract_inverted_index.intersection-over-union | 114 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5062901800 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I37461747 |
| citation_normalized_percentile.value | 0.74128762 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |