Abstract
Pavement deterioration and abnormal climate induced by global warming lead to a constant rise in the number of potholes. Accordingly, the loss cost for maintenance and accidents also increases. Therefore, it is necessary to develop a method of classifying pavement potholes and detecting their locations. This study proposes the pothole region extraction based on similarity evaluation scale classification using image processing. The proposed technique sets up a classification threshold appropriately by considering the structure, brightness, and other factors of the grayscale-converted image through SSIM (Structural Similarity Index Measure). It binarizes porthole images classified according to the threshold, and then extracts pothole regions through the threshold based segmentation. A conventional image classification method utilizes the rules found in objects or the label selected by a user. The proposed method can take into account detailed factors by comparing image similarity in the unit of pixel. According to the performance evaluation, the proposed classification method’s F1-score is 0.83, and its accuracy of pothole region extraction is 0.851. Therefore, with the proposed technique, it is possible to make classification in consideration of similarity between images. In addition, the proposed method makes it possible to detect the regions similar to actual potholes accurately.
Highlights
With the development of transportation means, more people use pavements
To extract the image quality recognized by human Structural Similarity (SSIM), which has a different approach from the two scale methods aforementioned, was developed
In the first performance evaluation, the accuracy of the proposed SSIM based image classification was evaluated, in the second evaluation, image segmentation regions were compared with real pothole regions in terms of a consistency ratio
Summary
With the development of transportation means, more people use pavements. Due to the sharp rise in pavement users and climate changes, the frequency of road damage is on the increase. Potholes can bring about big losses in terms of social and economic aspects and can cause human damage beyond physical damage For this reason, it is necessary to develop a method of detecting potholes accurately in order to deal with them. This study proposes the pothole region extraction based on similarity evaluation scale classification using image processing. With the uses of dilation and erosion of Morphological processing, an object’s unnecessary regions are removed, and the pixels of significant pothole region is expanded In this way, it is possible to extract a region similar to a real pothole. The first performance assessment evaluates accuracy through the classification of normal road images and porthole images based on SSIM. The second performance assessment evaluates the matching rate of pixels between the segmentation image based on the Threshold and the original image
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