AUTOMATIC DETECTION OF CRACKS IN ASPHALT PAVEMENT USING WEAK TYPE IMPROVED DEEP LEARNING

Abstract

In the asphalt pavement inspection, efficient and quantitative methods are required. So in recent years, several methods for automatically detecting cracks from pavement images has been proposed and its effectiveness has been shown. This paper have attempted to construct a system to efficiently improve the accuracy of crack detection by convolutional neural network which is a type of deep learning. It is generally considered that the accuracy of deep learning model is proportional to the amount of learning data. Therefore this paper have not simply increased the amount of learning data, but used only the data that satisfied the conditions. When the accuracy of the model obtained by this method has compared with that of the model learned from randomly selected data, it has confirmed that the accuracy of the model by this paper method has stably improved. In addition, the soundness judgment of pavement was carried out using the crack detection result of the model, and the result was mapped on the GIS.