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

Abstract

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.

Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

“…Study [ 19 ] developed Panthera, a robotic platform for road crack segmentation and garbage detection. However, their study does not address the limitations of using the mobile mapping system (MMS) for geotagging defects, nor does it explore the technique’s scalability across larger or varied pavement types [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

“…tion. However, their study does not address the limitations of using the mobile map system (MMS) for geotagging defects, nor does it explore the technique's scalability a larger or varied pavement types [40][41][42][43][44][45][46][47].…”

“…Negative 12,000 3000 15,000 system (MMS) for geotagging defects, nor does it explore the technique's scalability a larger or varied pavement types [40][41][42][43][44][45][46][47].…”

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

“…Kernel Principal Component Analysis Linearly inseparable samples in a low-dimensional space can be mapped to a highdimensional space by the KPCA. The PCA is performed in the high-dimensional space, and then, the results back to the original low-dimensional space are projected, thereby obtaining a reduced dimensionality representation of nonlinear samples [31].…”

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

“…Early implementation of the CV techniques for crack detection was to some extent limited to threshold-based approaches (e.g., pixel intensity was used as the feature) [2,3], and other hand-crafted feature-based approaches. Some of the prominent hand-crafted featureextraction techniques are wavelet features [4], Local Binary Pattern (LBP) [5], Digital Image Co-relation [6], Gabor filters [7], and so on. But these methods can only extract local patterns instead of global patterns, which pulls the detection results backward.…”