CNN-Based Road Damage Detection

Monitoring road degradation is an important activity that can assist in reducing maintenance costs and preventing accidents. In picture recognition tasks, road damage detection using Convolutional Neural Networks (CNNs) has exhibited outstanding results. In this paper, a CNN-based robot was tasked with identifying various types of road surface deterioration. Gathering and preprocessing images of damaged road surfaces, constructing and instructing the CNN architecture, and evaluating the CNN's performance on a test set are all part of this approach. Our suggested method is extremely accurate, around 90%, at detecting various types of road degradation, including cracks, potholes, and bumps. The findings demonstrate how CNNs can be utilized for identifying road degradation and improving road maintenance and safety.