Detection Method of Cracks in Expressway Asphalt Pavement Based on Digital Image Processing Technology

被引:4
|
作者
Fang, Hui [1 ]
He, Na [2 ]
Carpinteri, Andrea
机构
[1] Henan Prov Highway Engn Bur Grp Co Ltd, Zhengzhou 450052, Peoples R China
[2] Henan Polytech Univ, Sch Civil Engn, Jiaozuo 454000, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 22期
关键词
non subsampled contourlet transform; pavement cracks; feature extraction; support vector machine; damage detection;
D O I
10.3390/app132212270
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Considering the limitations of the current pavement crack damage detection methods, this study proposes a method based on digital image processing technology for detecting highway asphalt pavement crack damage. Firstly, a non-subsampled contourlet transform is used to enhance the image of highway asphalt pavement. Secondly, the non-crack regions in the image are screened, and the crack extraction is completed by obtaining and enhancing the crack intensity map. Finally, the features of cracks are extracted and input into the support vector machine for classification and recognition to complete the detection of cracks in highway asphalt pavement. The experimental results show that the proposed method can effectively enhance the quality of a pavement image and precisely extract a crack area from the image with a high level of damage detection accuracy.
引用
收藏
页数:17
相关论文
共 50 条
  • [21] Automated pavement detection and artificial intelligence pavement image data processing technology
    Shang, Jing
    Zhang, Allen A.
    Dong, Zishuo
    Zhang, Hang
    He, Anzheng
    AUTOMATION IN CONSTRUCTION, 2024, 168
  • [22] Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance
    Nhat-Duc Hoang
    Quoc-Lam Nguyen
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2018, 2018
  • [23] Calculation method of particle sizes in asphalt emulsion based on digital image processing
    Zhou, Ji
    Tian, Qiong
    Ying, Hong
    Rui, Yong-Qin
    Jianzhu Cailiao Xuebao/Journal of Building Materials, 2013, 16 (01): : 81 - 85
  • [24] Detection and Recognition of Pavement Cracks Based on Computer Vision Technology
    Lyasheva, Stella
    Tregubov, Vladimir
    Shleymovich, Mikhail
    2019 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING, APPLICATIONS AND MANUFACTURING (ICIEAM), 2019,
  • [25] Application of the Measurement Method of Building Surface Cracks Based on Image Processing Technology
    Wang, Zhenzhou
    IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE, 2024, 27 (03) : 8 - 12
  • [26] Reconstruction and evolution of 3D model on asphalt pavement surface texture using digital image processing technology and accelerated pavement testing
    Ji, XiaoPing
    Zhu, Shiyu
    Sun, Yunlong
    Li, Hangle
    Chen, Ye
    Chen, Yun
    ROAD MATERIALS AND PAVEMENT DESIGN, 2024, 25 (08) : 1694 - 1719
  • [27] Research on Asphalt Mixture Injury Digital Image Based on Enhancement and Segmentation Processing Technology
    Zhang, Shuwen
    Zhang, Xiaoning
    Wu, Zhiyong
    Shi, Liwan
    MECHANICAL ENGINEERING, MATERIALS SCIENCE AND CIVIL ENGINEERING II, 2014, 470 : 832 - 837
  • [28] Evaluation of Expressway Asphalt Pavement Performance Based on the Fuzzy Theory
    Zhao, Hongli
    Li, Ruiran
    Cui, Yang
    Zhou, Guangyu
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL SYMPOSIUM ON ADVANCES IN ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING (ISAEECE 2017), 2017, 124 : 69 - 74
  • [29] Positioning and detection of rigid pavement cracks using GNSS data and image processing
    Ahmed A. Nasrallah
    Mohamed A. Abdelfatah
    Mohamed I. E. Attia
    Gamal S. El-Fiky
    Earth Science Informatics, 2024, 17 : 1799 - 1807
  • [30] Positioning and detection of rigid pavement cracks using GNSS data and image processing
    Nasrallah, Ahmed A.
    Abdelfatah, Mohamed A.
    Attia, Mohamed I. E.
    El-Fiky, Gamal S.
    EARTH SCIENCE INFORMATICS, 2024, 17 (02) : 1799 - 1807