Road Damage Detection using Deep Learning

Abstract

Road damage occurs when the function and structure of road are unable to service the traffic above it optimally. In general, the damage is caused by flaws in planning and implementation, uneven maintenance, poor drainage, and poor road user behaviour. It has a negative impact on driving comfort, road safety, and vehicle condition, and it may cause a number of accidents. To address this issue, this study presents a Region-based Convolutional Neural Network (R-CNN) for locating the dangerous path. This type of neural network can find essential information in both time series and picture data is the RCNN. As a result, it is extremely useful for image-associate tasks including image identification, object categorization, and design recognition. A RCNN uses linear algebra methods such as matrix multiplication to discover patterns inside an image. Find the photographs first and pre-process them, then extract the features and choose them from the feature set of previously damaged images. Finally, categorise the captured photos to obtain the optimum result. When compared to other current approaches, the suggested method is more accurate.