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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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Related Experiment Video

Updated: Jun 16, 2025

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
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A lightweight ground crack rapid detection method based on semantic enhancement.

Bing Yi1, Qing Long2, Haiqiao Liu3

  • 1School of Materials and Chemical Engineering, Hunan Institute of Engineering, 411104, Hunan, China.

Heliyon
|August 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight method for rapid ground crack detection, enhancing semantic understanding. The new approach significantly reduces model size while improving detection accuracy and speed for complex cracks.

Keywords:
Crack detectionDeep learningPavement maintenanceSemantic enhancement

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Manual detection of complex-shaped ground cracks is costly and inefficient.
  • Existing automated methods may lack the necessary lightweight design for rapid deployment.

Purpose of the Study:

  • To develop a lightweight and efficient deep learning model for rapid ground crack detection.
  • To enhance feature extraction and information fusion for improved crack localization.

Main Methods:

  • Proposed a novel lightweight ground crack rapid detection method based on semantic enhancement.
  • Integrated Context Guided Block into the YOLOv8 backbone for improved feature extraction.
  • Utilized GSConv and VoV-GSCSP to create an efficient neck network for multi-feature fusion.
  • Optimized the detection head for precise target localization.

Main Results:

  • The proposed method demonstrated effective crack detection on the RDD-2022 dataset.
  • Achieved a 73.5% reduction in model parameters compared to YOLOv8.
  • Improved accuracy by 6.6%, F1 score by 4.3%, and Frames Per Second (FPS) by 116.

Conclusions:

  • The developed method is significantly more lightweight than YOLOv8.
  • Offers substantial improvements in detection performance and speed.
  • Possesses significant application value for automated ground crack monitoring.