Road Damage Dataset Evaluation Using YOLOv8 for Road Inspection System

Agus Mulyanto, Riri Fitri Sari, Abdul Muis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The ASTM D6433-18 standard is widely used internationally in the Road Inspection System (RIS) to assess pavement distress, covering type, severity, and quantity. Automated detection of pavement distress types using vision-based methods follows these standards and requires a dataset with 19 types of pavement distress. The Road Damage Dataset (RDD) 2018 is a publicly available collection comprising 9,053 images captured on Japanese roads, each annotated with eight distinct types of road damage. However, only four of these classifications align with the categories specified in ASTM standards, namely alligator cracking, joint-reflection cracking, longitudinal and transverse cracking, and potholes. This article aims to assess the viability of utilizing the RDD dataset for Road Inspection System (RIS) purposes in accordance with the ASTM D6433-18 standard. The methodology involves the automated re-annotation of the dataset utilizing YOLOv8 models known as pseudo-labeling, followed by an evaluation to ascertain its compatibility with RIS requirements. The results suggest that the RDD-18 dataset is not suitable for conducting RIS in adherence to the ASTM D6433-18 standard. The evaluation results demonstrate less-than-optimal accuracy, which is attributed to an imbalanced distribution of instances among classes and a requirement for improved image quality. Finally, it is highlighted that the RDD 2018 dataset lacks images representing 15 additional types of pavement distress crucial for RIS applications based on ASTM standards.

Original languageEnglish
Title of host publication2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages403-407
Number of pages5
ISBN (Electronic)9798350370058
DOIs
Publication statusPublished - 2024
Event16th International Conference on Computer and Automation Engineering, ICCAE 2024 - Hybrid, Melbourne, Australia
Duration: 14 Mar 202416 Mar 2024

Publication series

Name2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024

Conference

Conference16th International Conference on Computer and Automation Engineering, ICCAE 2024
Country/TerritoryAustralia
CityHybrid, Melbourne
Period14/03/2416/03/24

Keywords

  • ASTM D6433-18
  • Pseudo-labeling
  • RDD-2018
  • Road Inspection System (RIS)
  • YOLOv8

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