Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5 Article Swipe
Yongming Han
,
L. Wang
,
Youqing Wang
,
Zhiqiang Geng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jas.2023.124035
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jas.2023.124035
This letter presents an intelligent small sample defect detection of concrete surface using novel deep learning integrating the improved YOLOv5 based on the Wasserstein GAN (WGAN) enhancement algorithm. The proposed method is capable of producing top-notch data sets to address the issues of insufficient samples and substandard quality. Moreover, the proposed method can efficiently detect numerous minor flaws present in real concrete structures, thereby compensating for the drawbacks of current techniques in terms of real-time performance, practicality, and precision. The study findings reveal a noteworthy increase in the precision of the suggested approach when compared to other methods, reaching 86.2%. Defects in concrete structures significantly impact their durability, service life and safety. The primary challenge lies in the fact that many surface defect detection methods necessitate predetermined inspection targets and parameters, which are often difficult to meet in practice. Numerous techniques are available; but they lack practicality. Due to the presence of numerous small defects within concrete, conventional inspection methods require high levels of accuracy, which can result in inaccurate results. Consequently, a more flexible and precise approach is urgently required for detecting concrete defects. The proposed method outlined in this letter offers a viable solution to this challenge.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jas.2023.124035
- https://ieeexplore.ieee.org/ielx7/6570654/10415853/10415905.pdf
- OA Status
- bronze
- Cited By
- 10
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391305487
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Raw OpenAlex JSON
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https://doi.org/10.1109/jas.2023.124035Digital Object Identifier
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Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5Work 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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2024-01-29Full publication date if available
- Authors
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Yongming Han, L. Wang, Youqing Wang, Zhiqiang GengList of authors in order
- Landing page
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https://doi.org/10.1109/jas.2023.124035Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6570654/10415853/10415905.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6570654/10415853/10415905.pdfDirect OA link when available
- Concepts
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Computer science, Sample (material), Durability, Artificial intelligence, Reliability engineering, Engineering, Database, Chromatography, ChemistryTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 4, 2024: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 17, 22, 40, 49, 66, 87, 90, 117, 149 |
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| abstract_inverted_index.data | 36 |
| abstract_inverted_index.deep | 14 |
| abstract_inverted_index.fact | 118 |
| abstract_inverted_index.high | 161 |
| abstract_inverted_index.lack | 145 |
| abstract_inverted_index.lies | 115 |
| abstract_inverted_index.life | 109 |
| abstract_inverted_index.many | 120 |
| abstract_inverted_index.meet | 136 |
| abstract_inverted_index.more | 173 |
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| abstract_inverted_index.that | 119 |
| abstract_inverted_index.they | 144 |
| abstract_inverted_index.this | 190, 197 |
| abstract_inverted_index.when | 93 |
| abstract_inverted_index.based | 20 |
| abstract_inverted_index.flaws | 57 |
| abstract_inverted_index.minor | 56 |
| abstract_inverted_index.novel | 13 |
| abstract_inverted_index.often | 133 |
| abstract_inverted_index.other | 96 |
| abstract_inverted_index.small | 5, 153 |
| abstract_inverted_index.study | 80 |
| abstract_inverted_index.terms | 72 |
| abstract_inverted_index.their | 106 |
| abstract_inverted_index.using | 12 |
| abstract_inverted_index.which | 131, 165 |
| abstract_inverted_index.(WGAN) | 25 |
| abstract_inverted_index.86.2%. | 99 |
| abstract_inverted_index.YOLOv5 | 19 |
| abstract_inverted_index.defect | 7, 122 |
| abstract_inverted_index.detect | 54 |
| abstract_inverted_index.impact | 105 |
| abstract_inverted_index.issues | 41 |
| abstract_inverted_index.letter | 1, 191 |
| abstract_inverted_index.levels | 162 |
| abstract_inverted_index.method | 30, 51, 187 |
| abstract_inverted_index.offers | 192 |
| abstract_inverted_index.result | 167 |
| abstract_inverted_index.reveal | 82 |
| abstract_inverted_index.sample | 6 |
| abstract_inverted_index.viable | 194 |
| abstract_inverted_index.within | 155 |
| abstract_inverted_index.Defects | 100 |
| abstract_inverted_index.address | 39 |
| abstract_inverted_index.capable | 32 |
| abstract_inverted_index.current | 69 |
| abstract_inverted_index.defects | 154 |
| abstract_inverted_index.methods | 124, 159 |
| abstract_inverted_index.precise | 176 |
| abstract_inverted_index.present | 58 |
| abstract_inverted_index.primary | 113 |
| abstract_inverted_index.require | 160 |
| abstract_inverted_index.safety. | 111 |
| abstract_inverted_index.samples | 44 |
| abstract_inverted_index.service | 108 |
| abstract_inverted_index.surface | 11, 121 |
| abstract_inverted_index.targets | 128 |
| abstract_inverted_index.thereby | 63 |
| abstract_inverted_index.Numerous | 139 |
| abstract_inverted_index.approach | 92, 177 |
| abstract_inverted_index.compared | 94 |
| abstract_inverted_index.concrete | 10, 61, 102, 183 |
| abstract_inverted_index.defects. | 184 |
| abstract_inverted_index.findings | 81 |
| abstract_inverted_index.flexible | 174 |
| abstract_inverted_index.improved | 18 |
| abstract_inverted_index.increase | 85 |
| abstract_inverted_index.learning | 15 |
| abstract_inverted_index.methods, | 97 |
| abstract_inverted_index.numerous | 55, 152 |
| abstract_inverted_index.outlined | 188 |
| abstract_inverted_index.presence | 150 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 29, 50, 186 |
| abstract_inverted_index.quality. | 47 |
| abstract_inverted_index.reaching | 98 |
| abstract_inverted_index.required | 180 |
| abstract_inverted_index.results. | 170 |
| abstract_inverted_index.solution | 195 |
| abstract_inverted_index.urgently | 179 |
| abstract_inverted_index.Moreover, | 48 |
| abstract_inverted_index.accuracy, | 164 |
| abstract_inverted_index.challenge | 114 |
| abstract_inverted_index.concrete, | 156 |
| abstract_inverted_index.detecting | 182 |
| abstract_inverted_index.detection | 8, 123 |
| abstract_inverted_index.difficult | 134 |
| abstract_inverted_index.drawbacks | 67 |
| abstract_inverted_index.practice. | 138 |
| abstract_inverted_index.precision | 88 |
| abstract_inverted_index.producing | 34 |
| abstract_inverted_index.real-time | 74 |
| abstract_inverted_index.suggested | 91 |
| abstract_inverted_index.top-notch | 35 |
| abstract_inverted_index.algorithm. | 27 |
| abstract_inverted_index.available; | 142 |
| abstract_inverted_index.challenge. | 198 |
| abstract_inverted_index.inaccurate | 169 |
| abstract_inverted_index.inspection | 127, 158 |
| abstract_inverted_index.noteworthy | 84 |
| abstract_inverted_index.precision. | 78 |
| abstract_inverted_index.structures | 103 |
| abstract_inverted_index.techniques | 70, 140 |
| abstract_inverted_index.Wasserstein | 23 |
| abstract_inverted_index.durability, | 107 |
| abstract_inverted_index.efficiently | 53 |
| abstract_inverted_index.enhancement | 26 |
| abstract_inverted_index.integrating | 16 |
| abstract_inverted_index.intelligent | 4 |
| abstract_inverted_index.necessitate | 125 |
| abstract_inverted_index.parameters, | 130 |
| abstract_inverted_index.structures, | 62 |
| abstract_inverted_index.substandard | 46 |
| abstract_inverted_index.compensating | 64 |
| abstract_inverted_index.conventional | 157 |
| abstract_inverted_index.insufficient | 43 |
| abstract_inverted_index.performance, | 75 |
| abstract_inverted_index.Consequently, | 171 |
| abstract_inverted_index.practicality, | 76 |
| abstract_inverted_index.practicality. | 146 |
| abstract_inverted_index.predetermined | 126 |
| abstract_inverted_index.significantly | 104 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.9172337 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |