Deep Learning-Based Road Defect Detection Using an Improved YOLOv8 Model

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

Abstract

This study proposes a model with YOLOv8x as the core framework and targeted optimization to achieve low-cost road defect detection and classification.First, the diversity and robustness of the dataset were enriched through Data Augmentation technology, which effectively improved the model's ability to recognize different types of road defect after training.Secondly, for Convolutional Neural Network (CNN), the Feature Pyramid structure is introduced in this study, which enables the model to capture and identify the subtle defect features in road images more accurately, further improving the accuracy and effect of detection.The experimental results show that the improved scheme has achieved obvious results, the F1 score increased from 0.58350 of the traditional YOLOv8 to 0.62936.The model can identify various types of damaged pavement more comprehensively and accurately.It provides a more reliable and effective solution for road defect detection in various road conditions.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2025
EditorsMasayuki Nakajima, Chuan-Yu Chang, Chia-Hung Yeh, Jae-Gon Kim, Kemao Qian, Phooi Yee Lau
PublisherSPIE
ISBN (Electronic)9781510688124
DOIs
Publication statusPublished - 2025
Externally publishedYes
Event2025 International Workshop on Advanced Imaging Technology, IWAIT 2025 - Douliu City, Taiwan, Province of China
Duration: 6 Jan 20258 Jan 2025

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume13510
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2025 International Workshop on Advanced Imaging Technology, IWAIT 2025
Country/TerritoryTaiwan, Province of China
CityDouliu City
Period6/01/258/01/25

Keywords

  • Centralized Feature Pyramid
  • Inverted Residual Mobile Block
  • Object detection
  • Road defect detection
  • YOLOv8x

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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