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Related Experiment Video

Updated: Sep 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Road damage detection based on improved YOLO algorithm.

Luyao Ma1,2, Ming Chen3,4

  • 1Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, China.

Scientific Reports
|August 5, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances road damage detection using deep learning, improving accuracy for small defects and pavement condition assessment. The new YOLOv5 model with attention mechanisms offers a more efficient solution for infrastructure monitoring.

Keywords:
Attention mechanismDeep learningRoad breakageYOLOv5

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Area of Science:

  • Computer Science
  • Civil Engineering
  • Artificial Intelligence

Background:

  • Urbanization and increased transportation demand exacerbate road damage issues.
  • Traditional manual road inspection methods are inefficient, costly, and unable to meet current demands.
  • Deep learning-based road damage detection offers a cutting-edge, efficient solution.

Purpose of the Study:

  • To present an enhanced object detection algorithm for road damage detection.
  • To improve detection accuracy and localization capabilities, especially for small objects.
  • To integrate attention mechanisms and advanced loss functions for better performance.

Main Methods:

  • An enhanced object detection algorithm based on YOLOv5.
  • Integration of Channel Attention (CA) and Spatial Attention (SA) dual-branch attention mechanisms.
  • Utilization of Generalized Intersection over Union (GIoU) loss for bounding box regression.

Main Results:

  • The enhanced algorithm shows improved feature representation and bounding box localization.
  • Notable improvements were observed in small object detection and localization accuracy.
  • Experimental results indicate a 2.3% boost in retrieval rate, a 0.3 increase in average value, and a 0.7 improvement in F1 score compared to existing methods.
  • Pavement Condition Index (PCI) values were calculated, providing expected pavement evaluation results.

Conclusions:

  • The proposed YOLOv5-based algorithm with dual-branch attention and GIoU loss significantly enhances road damage detection.
  • The method offers superior accuracy and localization, particularly for challenging cases like small road defects.
  • This advanced approach provides a more efficient and effective solution for road infrastructure monitoring and assessment.