Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Architectural Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
CHEN, Gong, XIE. MICRO RENEWAL OF PUBLIC SPACE IN OLD COMMUNITIES BASED ON SHARING CONCEPT[J]. INDUSTRIAL CONSTRUCTION, 2020, 50(1): 80-83,90. doi: 10.13204/j.gyjz202001014
Citation: LU Peng, ZHAO Tiansong, WANG Jian, CHANG Haosong, ZHENG Yun, LIU Xiaolan. A Method for Detecting Surface Corrosion Degree of Steel Structures Based on Computer Vision[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(8): 133-139. doi: 10.3724/j.gyjzG23062009

A Method for Detecting Surface Corrosion Degree of Steel Structures Based on Computer Vision

doi: 10.3724/j.gyjzG23062009
  • Received Date: 2023-06-20
    Available Online: 2024-09-19
  • Traditional manual inspection methods for surface corrosion damage in steel structures are time-consuming, labor-intensive, and limited by the technical expertise of the inspection personnel. Computer vision technology provides a fast and accurate alternative method for detecting and classifying the surface corrosion on steel structures. Currently, commonly used methods for corrosion degree detection are based on convolutional neural network (CNN) structures. However, due to inherent flaws in the network structure, there is a problem of neglecting certain rust features in the image during corrosion degree classification, leading to incorrect detection results. A steel structure surface corrosion degree recognition method based on the Vision Transformer network structure was proposed. By introducing self-attention mechanisms (SA) during the feature extraction process, data integrity could be ensured. The proposed method was validated on a self-built dataset of rust severity images, achieving an accuracy ratio of 90% in corrosion degree classification. Furthermore, a steel structure surface corrosion degree detection method based on the sliding-window method was also proposed. This method involves segmenting the steel structure images to be inspected, utilizing a trained network structure for corrosion degree detection, and reassembling the detected images to achieve intelligent detection of corrosion degree on the surface of steel structures.
  • [1]
    GROSHEK I G, HEBDON M H. Galvanic corrosion of ASTM A1010 steel connected to common bridge steels[J]. Journal of Materials in Civil Engineering, 2020, 32(8), 04020193.
    [2]
    KERE K J, HUANG Q. Life-cycle cost comparison of corrosion management strategies for steel bridges[J]. Journal of Bridge Engineering, 2019, 24(4),0419007.
    [3]
    WANG N, ZHAO Q, LI S, et al. Damage classification for masonry historic structures using convolutional neural networks based on still images[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12): 1073-1089.
    [4]
    FELICIANO F F, LETA F R, MAINIER F B. Texture digital analysis for corrosion monitoring[J]. Corrosion Science, 2015, 93: 138-147.
    [5]
    LI Z, LIU Y, HAYWARD R, et al. Knowledge-based power line detection for UAV surveillance and inspection systems[C]//Image & Vision Computing New Zealand, Ivcnz, International Conference. 2009.
    [6]
    NIKOLIC J, BURRI M, REHDER J, et al. A UAV system for inspection of industrial facilities[C]//Aerospace Conference, 2013 IEEE. 2013.
    [7]
    BONNÍN-PASCUAL F, ORTIZ A. Detection of cracks and corrosion for automated vessels visual inspection[C]//Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence. l’Espluga de Francolí: Tarragona, 2010:20-22.
    [8]
    BENTO M P, DE MEDEIROS F N, PAULA JR I C, et al. Image processing techniques applied for corrosion damage analysis[C]//Proceedings of the XXII Brazilian Symposium on Computer Graphics and Image Processing. Rio de Janeiro: 2009.
    [9]
    AHUJA S K, SHUKLA M K. A survey of computer vision based corrosion detection approaches[C]//Information and Communication Technology for Intelligent Systems (ICTIS 2017). 2018:55-63.
    [10]
    MEDEIROS F N S, RAMALHO G L B, BENTO M P, et al. On the evaluation of texture and color features for nondestructive corrosion detection[J/OL]. EURASIP Journal on Advances in Signal Processing, 2010[2010-07-07].https://doi.org/DOI: 10.1155/2010/817473.
    [11]
    CHEN P H, SHEN H K, LEI C Y, et al. Fourier-transform-based method for automated steel bridge coating defect recognition[J]. Procedia Engineering, 2011, 14: 470-476.
    [12]
    SHEN H K, CHEN P H, CHANG L M. Automated steel bridge coating rust defect recognition method based on color and texture feature[J]. Automation in Construction, 2013, 31(5): 338-356.
    [13]
    KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks[J]. Advances In Neural Information Processing Systems, 2012, 25(2),3065386.
    [14]
    ATHA D J, JAHANSHAHI M R. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection[J]. Structural Health Monitoring, 2018,17(5):1110-1128.
    [15]
    WANG Y, SHEN X, WU K, et al. Corrosion grade recognition for weathering steel plate based on a convolutional neural network[J]. Measurement Science & Technology, 2022,33(9),095014.
    [16]
    ZHANG S, DENG X, LU Y, et al. A channel attention based deep neural network for automatic metallic corrosion detection[J]. Journal of Building Engineering, 2021, 42, 103046.
    [17]
    XU J, GUI C, HAN Q. Recognition of rust grade and rust ratio of steel structures based on ensembled convolutional neural network[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(10): 1160-1174.
    [18]
    DOSOVITSKIY A, BEYER L, KOLESNIKOV A,et al.An image is worth 16×16 words: transformers for image recognition at scale[C]//International Conference on Learning Representations.2021.
    [19]
    中国国家标准化管理委员会.涂覆涂料前钢材表面处理表面清洁度的目视评定 第1部分:未涂覆过的钢材表面和全面清除原有涂层后的钢材:GB/T 8923.1—2011[S].北京:中国标准出版社,2011.
    [20]
    SIMONYAN K, ZISSERMAN A.Very deep convolutional Networks for large-scale image recognition[J/OL].Computer Science, 2014[2014-09-04].https://doi.org/10.48550/arXiv.1409.1556.
    [21]
    SANDLER M, HOWARD A, ZHU M, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, 2018.
    [22]
    HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778.
  • Relative Articles

    [1]XUE Qianming, HUANG Yuehao, SHANG Yongtao. Research on Micro-Renewal and Optimization Design of Lanzhou Railway Station Area Under Catalyst Linkage[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(5): 86-94. doi: 10.3724/j.gyjzG23060709
    [2]ZHANG Xia, ZHAO Xue, LIAO Zixiang. Application of Affordance Theory to the Community-Based Renewal of Industrial Relics and Strategies: Taking Wuhan City as an Example[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(12): 45-53. doi: 10.13204/j.gyjzG23083006
    [3]JIN Liansheng, CHEN Chen. Protection and Renewal Strategies of Santaizi Worker’s Community in Shenyang from a Perspective of Community Co-Governance Systems[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(1): 72-81. doi: 10.13204/j.gyjzG21012706
    [4]ZHANG Hongbo, YANG Yujia. Deconstructive Study on Public Space of Jinjiang Timber Cabin Village in Jilin Based on the Pattern Language[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 64-73. doi: 10.13204/j.gyjzG22060804
    [5]REN Zhen, KOU Juntao, WANG Yu, CHI Miaomiao. Research on the Regeneration Design of Industrial Remain Sites from the Perspective of Landscape Urbanism: A Case Study of the Old Brewery in Pingyuan County[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(10): 17-22. doi: 10.13204/j.gyjzG22062304
    [6]CAO Ying, YANG Jinpeng, WANG Yu, ZHANG Nan. Protection and Reuse of Mining Heritage Based on Community Renewal: Taking the Zhongfu Mining Heritage in Jiaozuo as an Example[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(1): 52-58. doi: 10.13204/j.gyjzG20102504
    [7]LYU Chang, WEI Chunyu. TAKING TIANHAN CULTURAL PARK AS AN EXAMPLE: RESEARCH ON THE CURRENT SITUATION AND DESIGN OF CONTEMPORARY VILLAGE MUSEUM[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(10): 74-80. doi: 10.13204/j.gyjzg21022003
    [8]QIAO Zhi, JIA Xinxin, HUANG Jingfan, FAN Wenlu. STUDY ON THE AGING SPACE ACTIVATION AND FACILITIES RENEWAL OF XI'AN TEXTILE CITY INDUSTRIAL COMMUNITY FROM THE PERSPECTIVE OF COLLECTIVE MEMORY[J]. INDUSTRIAL CONSTRUCTION, 2020, 50(2): 89-97. doi: 10.13204/j.gyjz202002013
    [9]CHEN Jing, HAO Xinyi, YANG Li. STUDY ON THE SPATIAL FORM OF SILO-CAVE VILLAGE IN THE WEST OF HENAN[J]. INDUSTRIAL CONSTRUCTION, 2020, 50(5): 8-12. doi: 10.13204/j.gyjz202005002
    [12]Deng Yuanyuan, Chang Jiang. MICRO SPATIAL COGNITIVE OF INHABITANT IN OLD COMMUNITY: THE INVESTIGATION FOR THE WORKER COMMUNITY OF THE 2ND MACHINERY PLANT IN JIAWANG DISTRICT,XUZHOU[J]. INDUSTRIAL CONSTRUCTION, 2014, 44(05): 40-44.
    [13]Sun Jian, Zhao Lin. THE RENOVATION OF QINGDAO SMALL HARBOR[J]. INDUSTRIAL CONSTRUCTION, 2013, 43(1): 156-159. doi: 10.13204/j.gyjz201301035
    [14]Wang Xixi, Chen Xingzhu. RESUSCITATION OF THE HEART OF CITY:RENOVATION OF LES HALLES,PARIS[J]. INDUSTRIAL CONSTRUCTION, 2012, 42(3): 56-59. doi: 10.13204/j.gyjz201203011
    [15]Wang Lu, Xu Jia, Tuo Wanyong, Li Yuhua. ANALYSIS OF PLANNING AND SIGHT DESIGN FOR GANGHUA GARDEN[J]. INDUSTRIAL CONSTRUCTION, 2012, 42(11): 45-48. doi: 10.13204/j.gyjz201211010
    [16]Dong Jie, Su Jihong, Wang Shiyang, Zou Dan. CONSTRUCTION STRATEGY OF CONTEMPOARY INDUSTRIAL PARKS BASED ON VITALITY MOULDING[J]. INDUSTRIAL CONSTRUCTION, 2011, 41(8): 4-7. doi: 10.13204/j.gyjz201108002
    [17]He Wei. RESEARCH AND INTEGRATION DESIGN OF OLD AND NEW CAMPUS PUBLIC SPACE IN HUNAN UNIVERSITY[J]. INDUSTRIAL CONSTRUCTION, 2011, 41(5): 47-49. doi: 10.13204/j.gyjz201105011
    [18]Wang Yi, Chen Jing. THE EXPLORATION AND PRACTICE OF INDUSTRIAL PARKS UNDER THE CONCEPTION OF SUSTAINABLE DEVELOPMENT[J]. INDUSTRIAL CONSTRUCTION, 2008, 38(12): 37-40. doi: 10.13204/j.gyjz200812011
    [19]Shi Qi-lei. ARCHITECTURAL DESIGN OF COMPREHENSIVE MEDICAL BUILDING FOR THE PLAcS NO. 306 HOSPITAL[J]. INDUSTRIAL CONSTRUCTION, 2006, 36(10): 29-31. doi: 10.13204/j.gyjz200610009
    [20]Zhang Sanming, Wu Qian. RECONSTRUCTION DESIGN OF ACOUSTICAL ENVIRONMENT OF INTERIOR PUBLIC SPACE[J]. INDUSTRIAL CONSTRUCTION, 2006, 36(2): 31-33. doi: 10.13204/j.gyjz200602009
  • Cited by

    Periodical cited type(15)

    1. 高雅薇,孙伟,官卫华. 基于多主体治理视角的城市更新研究进展与展望. 现代城市研究. 2024(06): 1-7+45 .
    2. 余文志豪,孙靓,刘梦昭,姚彧之,蔡祎文. 基于空间激活的武汉保成路社区入口改造. 山西建筑. 2023(07): 39-42 .
    3. 王崎. 基于微更新的住区开放空间适老性研究进展及趋势. 低温建筑技术. 2023(04): 30-33 .
    4. 张思源. 老旧小区首层自发加建研究——以柳州机车车辆有限公司东社区为例. 城市建筑. 2023(22): 182-185 .
    5. 潘博,田从祥,王文斌. 基于“共享”理念下老旧社区公共空间更新探索——以荆州市荆州古城便河社区为例. 四川建材. 2022(02): 51-52+54 .
    6. 宋鹏波,孙涛,郑云峰. 基于UCD理念的老旧社区公共空间景观微改造创新设计研究——以武汉市武展社区为例. 中国勘察设计. 2022(09): 87-90 .
    7. 陈晓菲,冉圣林,马青松. 大街区视角下城镇老旧小区改造策略研究. 住区. 2022(04): 6-14 .
    8. 张恒瑜,张忠峰,赵红霞. 城市微更新背景下基于“共享”理念的老城区公共空间改造. 现代园艺. 2022(21): 95-97 .
    9. 陈明晨,李凯怡,何雪倩. 共享养老模式下老旧社区口袋公园的设计探析. 科学技术创新. 2022(36): 155-158 .
    10. 宁晓蕾. 共享理念下老旧社区公共空间微更新. 海峡科技与产业. 2022(12): 104-106 .
    11. 孟军. 社区微更新视角下南阳老旧社区体育设施优化配置研究. 体育风尚. 2021(02): 132-133 .
    12. 凌云. 社区更新中的可持续发展策略研究——以美国纽约为例. 建筑与文化. 2021(06): 58-59 .
    13. 黄芸璟,彭震宇. 基于城市闲置空间的智慧共享研究——以重庆市住宅空间为例. 国土资源信息化. 2021(04): 22-27+21 .
    14. 吴文勇. 垃圾分类背景下城市公共垃圾桶视觉设计研究. 包装工程. 2021(18): 287-291 .
    15. 李馨瞳. 西安市老旧社区微更新改造理念与策略研究. 绿色科技. 2020(18): 199-200+232 .

    Other cited types(40)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-042024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-0305101520
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 19.0 %FULLTEXT: 19.0 %META: 80.1 %META: 80.1 %PDF: 0.9 %PDF: 0.9 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 3.4 %其他: 3.4 %其他: 1.2 %其他: 1.2 %China: 1.2 %China: 1.2 %Hong Kong, China: 0.9 %Hong Kong, China: 0.9 %[]: 0.6 %[]: 0.6 %上海: 2.8 %上海: 2.8 %东莞: 0.6 %东莞: 0.6 %北京: 8.9 %北京: 8.9 %南京: 1.2 %南京: 1.2 %南通: 0.3 %南通: 0.3 %台州: 0.6 %台州: 0.6 %合肥: 0.6 %合肥: 0.6 %嘉兴: 0.3 %嘉兴: 0.3 %大连: 1.2 %大连: 1.2 %天津: 0.6 %天津: 0.6 %太原: 0.6 %太原: 0.6 %宿州: 0.3 %宿州: 0.3 %广州: 0.6 %广州: 0.6 %张家口: 0.9 %张家口: 0.9 %成都: 0.9 %成都: 0.9 %扬州: 0.6 %扬州: 0.6 %晋城: 0.3 %晋城: 0.3 %朝阳: 1.2 %朝阳: 1.2 %杭州: 3.7 %杭州: 3.7 %武汉: 1.2 %武汉: 1.2 %泰安: 0.3 %泰安: 0.3 %济南: 0.3 %济南: 0.3 %济宁: 0.3 %济宁: 0.3 %温州: 0.3 %温州: 0.3 %湖州: 0.9 %湖州: 0.9 %漯河: 1.2 %漯河: 1.2 %珠海: 0.3 %珠海: 0.3 %石家庄: 0.6 %石家庄: 0.6 %福州: 0.9 %福州: 0.9 %芒廷维尤: 45.9 %芒廷维尤: 45.9 %荆州: 0.3 %荆州: 0.3 %菏泽: 0.3 %菏泽: 0.3 %衢州: 0.3 %衢州: 0.3 %西宁: 4.9 %西宁: 4.9 %贵阳: 0.3 %贵阳: 0.3 %运城: 2.8 %运城: 2.8 %邯郸: 0.3 %邯郸: 0.3 %郑州: 3.1 %郑州: 3.1 %重庆: 0.3 %重庆: 0.3 %镇江: 0.3 %镇江: 0.3 %长沙: 0.6 %长沙: 0.6 %阳泉: 0.6 %阳泉: 0.6 %其他其他ChinaHong Kong, China[]上海东莞北京南京南通台州合肥嘉兴大连天津太原宿州广州张家口成都扬州晋城朝阳杭州武汉泰安济南济宁温州湖州漯河珠海石家庄福州芒廷维尤荆州菏泽衢州西宁贵阳运城邯郸郑州重庆镇江长沙阳泉

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (93) PDF downloads(7) Cited by(55)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return