Pothole detection using deep learning

Every vehicle, whether manual or automatic, relies on the quality of the roads they travel on to reach their destination safely. Damage to vehicles and even death can result from imperfections in the road, such as speed bumps and potholes. Consequently, accidents and vehicle damage can be lessened by identifying and describing these outliers. Due to large quantities of duplicated data and significantly polluted measurement noise, street photographs are inherently multivariate, making the identification of street irregularities more challenging. Using a YOLO Deep learning model, this research provides automated color image processing of road potholes from video frames or smartphone images. In order to make training and usage go more smoothly, a lightweight architecture was selected. It has seven interwoven layers that work together well. With no scaling at all, each and every pixel of the source image is utilized. To acquire the maximum amount of data possible, we employed the standard stride and pooling processes. Because of this, the created model can detect potholes better and warn drivers to be careful. The proposed method gathers vital data for pothole detection by reviewing previous studies in this area.