Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

被引:10
|
作者
Zoubir, Hajar [1 ]
Rguig, Mustapha [1 ]
El Aroussi, Mohamed [2 ]
Chehri, Abdellah [3 ]
Saadane, Rachid [2 ]
机构
[1] Hassania Sch Publ Works, ERIC LAGCET, BP 8108, Casablanca, Morocco
[2] Hassania Sch Publ Works, SIRC LaGeS, BP 8108, Casablanca, Morocco
[3] Royal Mil Coll Canada, Dept Math & Comp Sci, Kingston, ON K7K 7B4, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
concrete bridge cracks; feature extraction; dimensionality reduction; histograms of oriented gradients; uniform local binary patterns; kernel principal component analysis; IDENTIFICATION; TEXTURE;
D O I
10.3390/electronics11203357
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Bridges deteriorate over time, which requires the continuous monitoring of their condition. There are many digital technologies for inspecting and monitoring bridges in real-time. In this context, computer vision has extensively studied cracks to automate their identification in concrete surfaces, overcoming the conventional manual methods that rely on human judgment. The general framework of vision-based techniques consists of feature extraction using different filters and descriptors and classifier training to perform the classification task. However, training can be time-consuming and computationally expensive, depending on the dimension of the features. To address this limitation, dimensionality reduction techniques are applied to extracted features, and a new feature subspace is generated. This work used histograms of oriented gradients (HOGs) and uniform local binary patterns (ULBPs) to extract features from a dataset containing over 3000 uncracked and cracked images covering different patterns of cracks and concrete surface representations. Nonlinear dimensionality reduction was performed using kernel principal component analysis (KPCA), and three machine learning classifiers were implemented to conduct the classification. The experimental results show that the classification scheme based on the support-vector machine (SVM) model and feature-level fusion of the HOG and ULBP features after KPCA application provided the best results as an accuracy of 99.26% was achieved by the proposed classification framework.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] BRAIN TUMOR CLASSIFICATION ON MRI IMAGES BY USING CLASSICAL LOCAL BINARY PATTERNS AND HISTOGRAMS OF ORIENTED GRADIENTS
    Gottipati, Srinivas Babu
    Thumbur, Gowri
    SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2024, 25 (05): : 4165 - 4176
  • [2] Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors
    Hurney, Patrick
    Waldron, Peter
    Morgan, Fearghal
    Jones, Edward
    Glavin, Martin
    IET INTELLIGENT TRANSPORT SYSTEMS, 2015, 9 (01) : 75 - 85
  • [3] Sub Oriented Histograms of Local Binary Patterns for Smoke Detection and Texture Classification
    Yuan, Feiniu
    Shi, Jinting
    Xia, Xue
    Yang, Yong
    Fang, Yuming
    Wang, Rui
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2016, 10 (04): : 1807 - 1823
  • [4] Image classification using local binary patterns
    Pronin, S., V
    JOURNAL OF OPTICAL TECHNOLOGY, 2020, 87 (12) : 738 - 741
  • [5] Leaf Classification using Local Binary Pattern and Histogram of Oriented Gradients
    Janahiraman, Tiagrajah, V
    Yee, Lim Khar
    Der, Chen Soon
    Aris, Hazleen
    2019 7TH INTERNATIONAL CONFERENCE ON SMART COMPUTING & COMMUNICATIONS (ICSCC), 2019, : 79 - 83
  • [6] Hyperspectral image classification using K-plane clustering and kernel principal component analysis
    Sayeh Mirzaei
    Multimedia Tools and Applications, 2023, 82 : 47387 - 47403
  • [8] Classification of local ultraluminous infrared galaxies and quasars with kernel principal component analysis
    Papaefthymiou, Evangelos S.
    Michos, Ioannis
    Pavlou, Orestis
    Lesta, Vicky Papadopoulou
    Efstathiou, Andreas
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2022, 517 (03) : 4162 - 4174
  • [9] Comparison between local binary pattern histograms and principal component analysis algorithm in face recognition system
    Thakral, Abha
    Vohra, Akshat
    PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES FOR SMART NATION (SMARTTECHCON), 2017, : 973 - 978
  • [10] Classification of Image Database Using Independent Principal Component Analysis
    Kekre, H. B.
    Sarode, Tanuja K.
    Save, Jagruti K.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2013, 4 (07) : 109 - 116