Asphalt texture vector
It is because, if rehabilitation process is performed timely, pavement restoration cost can be saved by up to 80%. Moreover, the productive pavement surveying process significantly leads to economic gain. With a large number of road sections needed to be inspected routinely, the automation of the pothole detection becomes a pressing need for transportation agencies. The reason is that one pavement inspector can only inspect less than 10 km per day. Although this conventional method can help to acquire accurate evaluation of potholes, it also features low productivity in both data collection and data processing. In developing countries, the pavement pothole is often detected manually by inspectors of local transportation agencies during periodical field surveys. Generally, structure aging, heavy traffic condition, poor drainage, thin asphalt layer substructure, and weak substructure can be the causes of pothole appearance.
The reason is that this form of defect significantly delays traffic and brings about a hazardous condition for drivers.Ī pothole is commonly defined as a bowl-shaped depression on the pavement surface with a minimum plane diameter of 150 mm. 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. The process of road safety survey generally consists of the detection of the defects (e.g., cracks and potholes) existing on the road section and evaluation of the magnitude of the defects. Therefore, it is of practical need to improve the effectiveness of the asphalt pavement maintenance process.
The 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. The 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. Evaluating road condition is a crucial task of transportation agencies that are responsible for establishing maintenance schedules and allocating maintenance budgets. Roads are essential components of the national infrastructure.
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. In addition, the LS-SVM has achieved the highest classification accuracy rate (roughly 89%) and the area under the curve (0.96). 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%. 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). Image processing methods including Gaussian filter, steerable filter, and integral projection are utilized for extracting features from digital images. This study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface.