Detecting and Classification of Complex Roads Defects Using a Deep Learning Approach: A survey

Abstract

Road safety is one of the most important conditions for public driving safety around the world. This is done by monitoring the roads periodically to detect defects that can appear due to several factors affecting the asphalt, such as weather and accidents. Traditional road inspection methods are often time-consuming, labor-intensive and expensive due to the length of the methods. Deep learning (DL) has emerged as a promising technology for automated detection and classification of road defects, offering the potential for faster, more accurate, and less expensive inspections. This review paper provides a comprehensive overview of the state-of-the-art methods working on the principle of artificial intelligence for detecting defects on road surfaces. We will also discuss the different types of defect monitoring methods that differ from one method to another and furthermore, we will show the different datasets from oldest to newest with all their details that can be useful in DL. A review is then performed of various blind learning architectures and algorithms that have been used for defect detection and classification, incorporating generative adversarial networks (GANs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). Finally, we will provide a comparative analysis of the reviewed algorithms and methods by presenting the metrics used to choose the best technique, which is what these paper aims to do.