Damage Detection and Classification of Road Surfaces

Abstract

Abstract: Road damage refers to a condition where a road is not able to serve the traffic up to an optimal level. For understanding the causes and the process of identifying road damages, a thorough literature survey of various international and national journal, articles have been done and presented in the current study. The same thing has also been done to assess and understand the current deep learning models used in road damage detection. Various common factors are identified that cause road damage. The dataset is extracted from the AWS server which has a separate.tar.gz train and test dataset. The dataset is in .jpg format which consists of images taken from three countries India, Japan, Czech Republic. The dataset of mixed images is used to reduce the biases of the model and to increase its accuracy. The cleaning and analysis of the data are done where different types of damages were classified and the categories having the least images are removed and not considered for further analysis. Then the dimensions of the bounding box of train datasets are identified and the area is calculated to find the damages which are taking more area. Two models are developed using MobilenetSSD and YOLOV5 and both are compared to find the best model.