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Accuracy-Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection.

Jiahui Zhang1, Zoia Vladimirovna Beliaeva1, Yue Huang1

  • 1Institute of Civil Engineering and Architecture, Ural Federal University, St. Mira19, 620002 Yekaterinburg, Russia.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

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This study optimizes the YOLOv8 model for structural crack detection, enhancing accuracy and efficiency. The improved model offers faster, more precise identification of fine cracks in real-time engineering applications.

Area of Science:

  • Computer Vision
  • Deep Learning
  • Structural Health Monitoring

Background:

  • Deep learning models for structural crack detection face accuracy-efficiency trade-offs.
  • Real-time object detection algorithms like YOLO (You Only Look Once) offer speed but require optimization for complex feature representation.

Purpose of the Study:

  • To develop an optimized YOLOv8 model for enhanced accuracy and efficiency in structural crack detection.
  • To improve the detection of fine cracks while maintaining real-time performance.

Main Methods:

  • Enhanced YOLOv8 backbone with the SimAM attention mechanism for improved crack feature representation.
  • Incorporated a lightweight C3Ghost module to reduce model parameters and computation.
  • Replaced the standard neck with a bidirectional multi-scale feature fusion structure for enhanced efficiency.
Keywords:
C3GhostSimAMYOLOv8accuracy–efficiency trade-offattention mechanismcrack detectionfeature pyramid

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Main Results:

  • Achieved a mean Average Precision (mAP) of 88.7% at 0.5 IoU and 69.4% for mAP@0.5:0.95.
  • Reduced computational cost by 12.3% fewer Giga Floating Point Operations (GFlops).
  • Demonstrated faster inference speeds compared to the original model.

Conclusions:

  • The optimized YOLOv8 model effectively balances accuracy and efficiency for structural crack detection.
  • The proposed enhancements enable superior detection of fine cracks in real-time scenarios.
  • The model is well-suited for practical engineering applications requiring rapid and accurate structural assessments.