Abstract
This study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface. Image processing methods including Gaussian filter, steerable filter, and integral projection are utilized for extracting features from digital images. A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS‐SVM) and the artificial neural network (ANN). Experimental results obtained from a repeated subsampling process with 20 runs show that both LS‐SVM and ANN are capable methods for pothole detection with classification accuracy rate larger than 85%. In addition, the LS‐SVM has achieved the highest classification accuracy rate (roughly 89%) and the area under the curve (0.96). Accordingly, the proposed AI approach used with LS‐SVM can be very potential to assist transportation agencies and road inspectors in the task of pavement pothole detection.
Highlights
Roads are essential components of the national infrastructure
Evaluating road condition is a crucial task of transportation agencies that are responsible for establishing maintenance schedules and allocating maintenance budgets [1]. e correlation of road deterioration and the increasing number of traffic accidents leads to the fact that road safety has become a common concern in many countries [2]. e problem of asphalt road degradation has a very negative impact on the economic development for developing countries where financial resource for pavement maintenance is often insufficient. erefore, it is of practical need to improve the effectiveness of the asphalt pavement maintenance process
Because artificial neural network (ANN) and least squares support vector machine (LS-support vector machine (SVM)) are supervised learning algorithms, a data set of asphalt pavement images with ground truth conditions of pothole and nonpothole has to be collected for model training and validation. is study has collected images of asphalt pavement using a digital camera during field surveys. e two class labels of nonpothole and pothole are assigned by the inspector
Summary
Roads are essential components of the national infrastructure. Evaluating road condition is a crucial task of transportation agencies that are responsible for establishing maintenance schedules and allocating maintenance budgets [1]. e correlation of road deterioration and the increasing number of traffic accidents leads to the fact that road safety has become a common concern in many countries [2]. e problem of asphalt road degradation has a very negative impact on the economic development for developing countries where financial resource for pavement maintenance is often insufficient. erefore, it is of practical need to improve the effectiveness of the asphalt pavement maintenance process. Among several forms of pavement distresses, potholes are important indicators of the road defects, and they should be detected in a timely manner for the tasks of asphalt-surfaced pavement maintenance and rehabilitation [4]. The pavement pothole is often detected manually by inspectors of local transportation agencies during periodical field surveys. This conventional method can help to acquire accurate evaluation of potholes, it features low productivity in both data collection and data processing. Koch et al [14] established a pothole detection model that relies on the techniques of texture extraction and comparison between pothole pixels and healthy pavement pixels. Experimental results and performance comparison are reported in the fourth section, followed by conclusions of the study in the nal section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
