Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Abstract

Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency.

An automatic image processing based on Hough transform algorithm for pavement crack detection and classification

“…The texture information is missing for potholes covered by sandy soil. (Gao et al, 2020) Using digital image correlation To detect cracks and measure crack kinematics…”

Image processing-based automatic detection of asphalt pavement rutting using a novel metaheuristic optimized machine learning approach

“…The network of asphalt pavement road is definitely the largest component of infrastructure and its essentiality is reflected by the influence on daily activities of people. Therefore, the pavement condition strongly affects the safety of vehicle passengers and any degradations of road surface can bring about multiple negative influences on vehicle passengers as well as economic development of local societies (Gao et al 2020;Hadjidemetriou et al 2020;Hadjidemetriou et al 2018;Liu et al 2017). Many countries around the globe are currently focusing on maintaining the acceptable road condition.…”

Low altitude multispectral mapping for road defect detection

“…A study of this potential method will be carried not much longer. Gao et al (2020) has proposed the method Library for support vector machine (LIBSVM) to extract potholes on the cement concrete pavement. This study also used different classifiers such as otsu, edge detection, K-mean and watershed algorithm.…”