You are being redirected because this document is part of your ASTM Compass® subscription.
    This document is part of your ASTM Compass® subscription.

    If you are an ASTM Compass Subscriber and this document is part of your subscription, you can access it for free at ASTM Compass
    Volume 47, Issue 4 (February 2019)

    Recognition of Concrete and Gray Brick Based on Color and Texture Features

    (Received 17 July 2018; accepted 18 October 2018)

    Published Online: 15 February 2019

    CODEN: JTEVAB

      Format Pages Price  
    PDF (1.11 MB) 14 $25   ADD TO CART

    Cite this document

    X Add email address send
    X
      .RIS For RefWorks, EndNote, ProCite, Reference Manager, Zoteo, and many others.   .DOCX For Microsoft Word



    Abstract

    Identification and classification of construction waste are two important aspects of construction waste recycling. This study proposes a method based on image processing to identify gray brick and concrete, the most difficult types to identify that have the highest percentage of construction waste. By combining color features with texture features, machine learning algorithms are used for training and recognition. We paid great attention to the comparison of the performance of different color models, which includes Red, Green, Blue (RGB), Hue, Saturation, Value (HSV), and Lab. We found that gray histograms and color moments were suitable as color features of concrete and gray bricks. Meanwhile, the eigenvalues of the gray-level co-occurrence matrix (GLCM) were also discussed. Contrast, angular second moment, inverse different moment, and correlation in the five eigenvalues of GLCM were selected as texture features via experiments. We used three machine learning algorithms to train the extracted data. The results showed that the extreme learning machine had the lowest accuracy (96.25 %), whereas the support vector machine and back propagation algorithm had higher accuracy of 96.875 % and 98.125 %, respectively. The online testing had the accuracy of 95 %, indicating that the selected features are effective, and the accuracy can meet the engineering needs.

    Author Information:

    Zhuang, Jiangteng
    Department of Mechanical Engineering, Huaqiao University, Xiamen,

    Yang, Jianhong
    Department of Mechanical Engineering, Huaqiao University, Xiamen,

    Fang, Huaiying
    Department of Mechanical Engineering, Huaqiao University, Xiamen,

    Xiao, Wen
    Department of Mechanical Engineering, Huaqiao University, Xiamen,

    Ku, Yuedong
    Department of Mechanical Engineering, Huaqiao University, Xiamen,


    Stock #: JTE20180523

    ISSN:0090-3973

    DOI: 10.1520/JTE20180523

    Author
    Title Recognition of Concrete and Gray Brick Based on Color and Texture Features
    Symposium ,
    Committee E12