Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM

Chen, Cheng, Seo, Hyungjoon, Jun, Chang Hyun and Zhao, Y (2022) Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM. INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 23 (9). pp. 3274-3283.

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Abstract

A new crack detection approach based on local binary patterns (LBP) with support vector machine (SVM) was proposed in this paper. The propsed algorithm can extract the LBP feature from each frame of the video taken from the road. Then, the dimension of the LBP feature spaces can be reduced by Principal Component Analysis(PCA). The simplified samples are trained to be decided the type of crack using Support Vector Machine(SVM). In order to reflect the directional imformation in detail, the LBP processed image is devided into nine sub-blocks. In this paper, driving tests were performed 10 times and 12,000 image data were applied to the proposed algorithm. The average accuracy of the proposed algorithm with sub-blocks is 91.91%, which is about 6.6% higher than the algorithm without sub-blocks. The LBP-PCA with SVM applying sub-blocks reflects the directional information of the crack so that it has high accuracy of 89.41% and 88.24%, especially in transverse and longitudinal cracks. In the performance analysis of different crack classifiers, the F-Measure, which considered balance between the precision and the recall, of alligator cracks classifier was the highest at 0.7601 and hence crack detection performance is higher than others.

Item Type:	Article
Uncontrolled Keywords:	Crack detection, local binary patterns (LBP), Support vector machine (SVM), Principal component analysis (PCA), machine learning, sub-block
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	17 Sep 2021 08:32
Last Modified:	18 Jan 2023 21:28
DOI:	10.1080/10298436.2021.1888092
Open Access URL:	https://www.tandfonline.com/doi/full/10.1080/10298...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3137370

Available Versions of this Item

• Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM. (deposited 10 Jun 2021 09:48)
  • Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM. (deposited 17 Sep 2021 08:32) [Currently Displayed]