Abstract
During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.
Highlights
During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. is study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes
Using two-dimensional images, patched and unpatched potholes may have similar shapes. erefore, this study relies on image texture descriptors to delineate these two objects of interest. e texture descriptors of statistical measurement of color channels, the graylevel cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads
To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. e SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes
Summary
During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. is study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. In many regions in the world, economic growth is correlated with the extension of asphalt pavement networks Since these networks are constantly expanded in recent decades, maintaining them becomes a costly and arduous task especially for developing countries like Vietnam. It is because the financial resources of developing countries are often restricted and central governments or provincial authorities are struggling to find a balance between the funding used to construct new road networks and the funding needed to recover deteriorated existing ones. Maintained asphalt pavements lead to a vast number of traffic accidents. Erefore, pavement roads damaged by potholes must be quickly identified and maintained to recover smooth-running surfaces [5] Complexity caused by the removal of surfacing materials (refer to Figure 1). is type of defect causes a sudden change in road elevation and creates hazardous situations for drivers especially in the cases of inclement weather conditions (e.g., heavy rainfall). erefore, pavement roads damaged by potholes must be quickly identified and maintained to recover smooth-running surfaces [5]
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