Faster R-CNN Structure for Computer Vision-based Road Pavement Distress Detection

Smart cities can be controlled in all aspects and it is desired to have a structure that is planned to have controllable feedback. Asphalt is generally used as pavement material on roads that provide transportation of vehicles such as cars and buses on the highway. Asphalt material is deformed due to weather conditions, heavy vehicle passage. In the smart city structure, similar deformations should be reported to the relevant unit. In this article, it was tried to determine the deteriorations on the asphalt by selecting the data set obtained from a region with image processing methods and deep learning technique. With the action camera placed in an automobile, a total of 4315 asphalt images with various distortions and without any deterioration were used as dataset. The dataset was classified using a pixel-based Faster Region-based Convolutional Neural Network. Accuracy, precision and sensitivity values were used to make the performance result obtained as a result of classification meaningful. With this proposed method, the average accuracy rate was 93.2%. With these results, an approach that can automatically detect asphalt deterioration in smart city structures has been developed.