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MS-YOLOv8-Based Object Detection Method for Pavement Diseases.

Zhibin Han1, Yutong Cai1, Anqi Liu1

  • 1School of Transportation, Jilin University, Changchun 130022, China.

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|July 27, 2024
PubMed
Summary
This summary is machine-generated.

This study presents MS-YOLOv8, an improved algorithm for detecting pavement diseases. It enhances accuracy and adaptability for road maintenance, offering a more efficient automated solution.

Keywords:
deformable large kernel attentionmulti-scale dilated attentionobject detectionpavement diseases

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Pavement disease detection is vital for road maintenance.
  • Traditional methods are inefficient and inaccurate.
  • Automated detection systems are needed.

Purpose of the Study:

  • Introduce MS-YOLOv8, an enhanced pavement disease recognition algorithm.
  • Improve detection accuracy and adaptability to varied pavement conditions.
  • Provide an automated solution for road defect detection.

Main Methods:

  • Modified the YOLOv8 model with three novel mechanisms: Deformable Large Kernel Attention (DLKA), Large Separable Kernel Attention (LSKA), and Multi-Scale Dilated Attention with Spatially Weighted Dilated Convolution (SWDA).
  • DLKA dynamically adjusts convolution kernels for multi-scale targets.
  • LSKA enhances feature extraction, and SWDA improves background distinction and precision.

Main Results:

  • MS-YOLOv8 increased background classification accuracy by 6%.
  • Overall precision improved by 1.9%, and mean Average Precision (mAP) by 1.4%.
  • Specific disease detection mAP increased by 2.9% with comparable detection speeds.

Conclusions:

  • MS-YOLOv8 significantly enhances pavement disease detection accuracy and adaptability.
  • The novel attention mechanisms improve multi-scale feature extraction and precision.
  • This algorithm offers a valuable reference for automated road defect detection systems.