Road Surface Defect Detection -- From Image-based to Non-image-based: A
  Survey
Jongmin Yu Jiaqi Jiang Sebastiano Fichera Paolo Paoletti Lisa Layzell Devansh Mehta Shan Luo
Ensuring traffic safety is crucial, which necessitates the detection and
prevention of road surface defects. As a result, there has been a growing
interest in the literature on the subject, leading to the development of
various road surface defect detection methods. The methods for detecting road
defects can be categorised in various ways depending on the input data types or
training methodologies. The predominant approach involves image-based methods,
which analyse pixel intensities and surface textures to identify defects.
Despite their popularity, image-based methods share the distinct limitation of
vulnerability to weather and lighting changes. To address this issue,
researchers have explored the use of additional sensors, such as laser scanners
or LiDARs, providing explicit depth information to enable the detection of
defects in terms of scale and volume. However, the exploration of data beyond
images has not been sufficiently investigated. In this survey paper, we provide
a comprehensive review of road surface defect detection studies, categorising
them based on input data types and methodologies used. Additionally, we review
recently proposed non-image-based methods and discuss several challenges and
open problems associated with these techniques.