mlsanityData to 14 Oct 2026

Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey

arXiv 2402.04297 ↗First posted 6 Feb 2024cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 31 of its 167 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

31 of 167 references.

References · 31

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19 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasetscited by 5
  2. 2025What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studiescited by 1
  3. 2025Surface Defect Detection with Gabor Filter Using Reconstruction-Based Blurring U-Net-ViTcited by 0
  4. 2026Training-Free Tunnel Defect Inspection and Engineering Interpretation via Visual Recalibration and Entity Reconstructioncited by 0