Article(id=1223210597974655906, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221615, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1659628800000, receivedDateStr=2022-08-05, revisedDate=1662393600000, revisedDateStr=2022-09-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1769565732710, onlineDateStr=2026-01-28, pubDate=1687622400000, pubDateStr=2023-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769565732710, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769565732710, creator=13701087609, updateTime=1769565732710, updator=13701087609, issue=Issue{id=1223210584024400210, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='6', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769565729385, creator=13701087609, updateTime=1769593153259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223325608164348105, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223325608164348106, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=137, endPage=141, ext={EN=ArticleExt(id=1223210598264062898, articleId=1223210597974655906, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Surface Defect Detection of Hydraulic Concrete Structures Based on Deep Learning, columnId=1222925283779400191, journalTitle=Water Resources and Power, columnName=WATER CONSERVANCY AND HYDROPOWER ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

Early and timely defect detection is essential to ensure the safe operation of hydraulic concrete structures. The deep learning-based computer vision method does not require complex manual feature engineering, and can automatically determine the category of structural defects in remote images, overcoming the shortcomings of traditional manual vision that are labor-intensive, subjective and prone to errors. Inspired by this, this paper proposes a deep learning-based defect detection method, which introduces attention mechanism into the ResNeXt50 network to adaptively recalibrate the channel-level feature responses, so that the model can pay more attention to the defect information in the image and enhance the feature extraction ability. Test results show that the proposed method can achieve an F1 score of 88.0%, and realize a good classification effect for common concrete defects.

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早期和及时的缺陷检测对于保证水工混凝土结构的安全运行至关重要。基于深度学习的计算机视觉方法无需复杂的手工特征工程,可以在远程图像中自动判定结构缺陷类别,克服了传统人工视觉劳动强度大、主观性强且易出错的缺点。受此启发,提出了一种基于深度学习的缺陷检测方法,在ResNeXt50网络中引入注意力机制,以自适应地重新校准通道级特征响应,使模型更加关注图像中的缺陷信息,增强特征提取能力。测试结果表明,所提方法可以实现88.0%的F1分数,对于常见混凝土缺陷可以实现较好的分类效果。

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苏超(1960-),男,博士、教授、博导,研究方向为水工结构缺陷检测与仿真计算,E-mail:
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曹国金(1973-),男,高级工程师,研究方向为水利工程技术管理,E-mail:

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曹国金(1973-),男,高级工程师,研究方向为水利工程技术管理,E-mail:

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曹国金(1973-),男,高级工程师,研究方向为水利工程技术管理,E-mail:

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基于深度学习的水工混凝土结构表面缺陷检测
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曹国金 1 , 苏超 2 , 王文君 2
水电能源科学 | 水利水电工程 2023,41(6): 137-141
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水电能源科学 | 水利水电工程 2023, 41(6): 137-141
基于深度学习的水工混凝土结构表面缺陷检测
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曹国金1 , 苏超2 , 王文君2
作者信息
  • 1.广州市流溪河灌区管理中心,广东 广州 510920
  • 2.河海大学水利水电学院,江苏 南京 210024
  • 曹国金(1973-),男,高级工程师,研究方向为水利工程技术管理,E-mail:

通讯作者:

苏超(1960-),男,博士、教授、博导,研究方向为水工结构缺陷检测与仿真计算,E-mail:
Surface Defect Detection of Hydraulic Concrete Structures Based on Deep Learning
Guo-jin CAO1 , Chao SU2 , Wen-jun WANG2
Affiliations
  • 1.Guangzhou Liuxihe Irrigation District Management Center, Guangzhou 510920, China
  • 2.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China
出版时间: 2023-06-25 doi: 10.20040/j.cnki.1000-7709.2023.20221615
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早期和及时的缺陷检测对于保证水工混凝土结构的安全运行至关重要。基于深度学习的计算机视觉方法无需复杂的手工特征工程,可以在远程图像中自动判定结构缺陷类别,克服了传统人工视觉劳动强度大、主观性强且易出错的缺点。受此启发,提出了一种基于深度学习的缺陷检测方法,在ResNeXt50网络中引入注意力机制,以自适应地重新校准通道级特征响应,使模型更加关注图像中的缺陷信息,增强特征提取能力。测试结果表明,所提方法可以实现88.0%的F1分数,对于常见混凝土缺陷可以实现较好的分类效果。

水工混凝土结构  /  缺陷检测  /  深度学习  /  卷积神经网络  /  注意力机制

Early and timely defect detection is essential to ensure the safe operation of hydraulic concrete structures. The deep learning-based computer vision method does not require complex manual feature engineering, and can automatically determine the category of structural defects in remote images, overcoming the shortcomings of traditional manual vision that are labor-intensive, subjective and prone to errors. Inspired by this, this paper proposes a deep learning-based defect detection method, which introduces attention mechanism into the ResNeXt50 network to adaptively recalibrate the channel-level feature responses, so that the model can pay more attention to the defect information in the image and enhance the feature extraction ability. Test results show that the proposed method can achieve an F1 score of 88.0%, and realize a good classification effect for common concrete defects.

hydraulic concrete structure  /  defect classification  /  deep learning  /  convolutional neural network  /  attention mechanism
曹国金, 苏超, 王文君. 基于深度学习的水工混凝土结构表面缺陷检测. 水电能源科学, 2023 , 41 (6) : 137 -141 . DOI: 10.20040/j.cnki.1000-7709.2023.20221615
Guo-jin CAO, Chao SU, Wen-jun WANG. Surface Defect Detection of Hydraulic Concrete Structures Based on Deep Learning[J]. Water Resources and Power, 2023 , 41 (6) : 137 -141 . DOI: 10.20040/j.cnki.1000-7709.2023.20221615
2023年第41卷第6期
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doi: 10.20040/j.cnki.1000-7709.2023.20221615
  • 接收时间:2022-08-05
  • 首发时间:2026-01-28
  • 出版时间:2023-06-25
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  • 收稿日期:2022-08-05
  • 修回日期:2022-09-06
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作者信息
    1.广州市流溪河灌区管理中心,广东 广州 510920
    2.河海大学水利水电学院,江苏 南京 210024

通讯作者:

苏超(1960-),男,博士、教授、博导,研究方向为水工结构缺陷检测与仿真计算,E-mail:
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2种不同金属材料的力学参数

Family
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Number of
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Number of
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种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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