A review on computer vision and machine learning techniques for automated road surface defect and distress detection

Abstract

As the pace grows in the development of image processing techniques and the current applications rise in machine learning and deep learning techniques for visual inspections and physical assessment, this article reviews the existing literature. It provides a detailed synthesis of the overview of surface pavement conditions, computer-vision-based technologies for road damage detection, various datasets and data collection methods. We analyse and compare different machine-learning methods and models proposed in the literature and identify challenges that need to be addressed in the future in road surface defect detection.

An End-to-End Computer Vision System Based on Deep Learning for Pavement Distress Detection and Quantification

“…These systems excel in extracting relevant features from pavement images. The most common approaches perform image classification, segmentation, and object detection [10]. Classification algorithms predict the type of defect present in an image [11].…”