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

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.

A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis

“…Short-time fast Fourier transform (STFT) Gabor (1946) improved the Fourier transform by introducing the 'window' concept (Cohn 1995). The short-time Fourier transform (STFT) is one of the most commonly used methods that the signal to be transformed is multiplied by a non-zero window function and the window function shifts along the axis of time (Chen et al 2019a(Chen et al , 2019b(Chen et al , 2021. The Fourier Transform is implemented in every window and the obtained spectrum can be expanded into a two-dimensional image which can reflect the frequency change over time.…”

Crack Detection in Frozen Soils Using Infrared Thermographic Camera

“…Afshani, et al [ 13 ] conducted a study to detect defects in the lining of a tunnel with an infrared thermal camera. Recently, deep learning and machine learning analyses were applied to find a crack in infrastructures [ 14 , 15 ]. Moreover, the three-dimensional monitoring system was applied for monitoring the displacement and tilt of infrastructure using laser scanning [ 16 ].…”

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

“…Automated pavement detection has undergone several significant technological changes, and digital image-based methods have been widely used for pavement crack detection and segmentation. The difference in grayscale values of crack pixels and background of digital images makes segmentation as well as detection logical [10]. Other factors such as lighting conditions, asphalt oil markings, and pavement markings, make pavement crack classification and segmentation are challenging as well.…”

“…Zou et al [12] proposed a crack tree noting the effect of lighting on pavement detection and proposed a shadow removal algorithm before crack extraction to eliminate the effect of shadows on the extraction results. However, the crack recognition requires the assistance of some machine learning algorithms such as SVM (Support Vector Machine), RBF (Radial Basis Function), KNN (K-Nearest Neighbor) and Random Decision Forest [10,13,14]. Also, statistical features, gray-level features, texture and shape features of cracked images are increasingly used for feature extraction of the images.…”