Road Damage Detection and Classification

Abstract

Abstract: This is a road damage detection desktop application. In the subject of transportation engineering, identifying road damage early on is essential because it can save maintenance costs and avoid accidents. Deep learning methods have demonstrated encouraging performance in a number of computer vision tasks recently, including the detection of road damage. In this study, we suggest a region-based convolutional neural network (R-CNN)-based method for detecting road degradation. Using a publicly accessible dataset of road photos with different kinds of damage, such as cracks, potholes, and patches, we trained our R-CNN. Our approach outperformed cutting-edge techniques with an accuracy of 85% in identifying road damage.