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YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model.

Jiayi Zeng1, Han Zhong2

  • 1College of information and Network Safety, People's Public Security University of China, Beijing, 100038, China.

Scientific Reports
|May 27, 2024
PubMed
Summary

This study introduces YOLOv8-PD, a lightweight road damage detection algorithm. It enhances accuracy and efficiency for pavement distress identification, improving road safety.

Keywords:
Attention mechanismGhostNetLSCD-HeadPavement distressesYOLOv8-PD

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Road safety is paramount, necessitating accurate and efficient road damage detection.
  • Existing methods struggle with multi-scale pavement distresses and high computational costs.
  • Automated detection systems are crucial for timely maintenance and infrastructure management.

Purpose of the Study:

  • To develop an improved, lightweight road damage detection algorithm (YOLOv8-PD) based on YOLOv8n.
  • To enhance the performance of pavement distress detection, addressing multi-scale challenges and reducing computational load.
  • To provide a robust and efficient solution for automated road damage identification.

Main Methods:

  • Proposed a novel BOT module for extracting global information and handling large-span crack features.
  • Integrated a large separable kernel attention (LKSA) mechanism to boost detection accuracy.
  • Developed a C2fGhost block in the neck network for efficient feature extraction of complex damages.
  • Introduced a lightweight shared convolution detection head (LSCD-Head) to improve feature expressiveness and reduce parameters.

Main Results:

  • The YOLOv8-PD model achieved 2.3M parameters and 6.1 GFLOPs, reducing baseline quantities by ~74%.
  • Achieved a 1.4 percentage point mAP improvement on the RDD2022 dataset compared to the baseline.
  • Demonstrated a 4.2% mAP increase on the RoadDamage dataset, indicating good robustness.

Conclusions:

  • The proposed YOLOv8-PD algorithm offers a significant improvement in lightweight road damage detection.
  • The method effectively addresses multi-scale pavement distresses with enhanced accuracy and reduced computational cost.
  • YOLOv8-PD provides a valuable reference for developing advanced automatic pavement distress detection systems.