DEFECT DETECTION ON ASPHALT PAVEMENT BY DEEP LEARNING

The importance of road infrastructure to the economy of any nation cannot be overemphasized, however, it is not easy to maintain it properly, the increase in maintenance and repair expenditures are issues of concern coupled with the constantly increasing number of roads. Since the inspection of pavements is particularly difficult, an efficient inspection method is required. In this study, a method for detecting damage in asphalt pavements was developed using one of the deep learning techniques, YOLOv3. YOLOv3 is a method for detecting the position and type of an object from an input image, which fits the purpose of this study. The developed method can distinguish between longitudinal crack, transverse crack, alligator crack, and pothole. To confirm the accuracy of the developed method, images of pavements acquired on National Route 4 using were analyzed. From the analysis, it is found that the precision value is 0.7 and the average IoU is 50.39%. From the visualization of the analysis results, it was found that this method based on YOLOv3 was able to detect the damage with good accuracy. This is a significant improvement and can help shape the entire road Inspection procedures.