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Microcracking in Concrete

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 27, 2026

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

YOLO-Crack: Geometry-Guided Real-Time Crack Detection Framework Toward Edge Deployment.

Zhe Wei1, Rui Wang1, Rong Dai1

  • 1School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan 618307, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO-Crack, an efficient object detection system for mobile crack inspection. It enhances real-time performance on edge devices by optimizing for slender crack geometry.

Keywords:
TensorRT deploymentYOLOv11attention mechanismcrack detectiongeometric constraints

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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Related Experiment Videos

Last Updated: Jun 27, 2026

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
05:30

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Mobile crack detection faces challenges with slender targets and real-time edge processing.
  • General object detectors struggle with these specific constraints.

Purpose of the Study:

  • To develop an efficient, closed-loop solution for mobile crack detection on edge devices.
  • To improve accuracy and real-time performance for slender crack identification.

Main Methods:

  • Proposed YOLO-Crack, integrating geometry-statistics-driven module design.
  • Introduced crack-aware cross-dimensional fusion attention (CFCA) and dual-path feature enhancement (DFEM) modules.
  • Implemented empirical smooth quality window adjustment with shape consistency regularization for bounding-box regression.

Main Results:

  • YOLO-Crack achieved 78.8% precision, 51.4% recall, and 65.7% mAP@0.5 on the Crack500 dataset.
  • Outperformed YOLOv11n baseline by 4.2% in precision, 1.7% in recall, and 2.9% in mAP@0.5.
  • Achieved 25.5 FPS on a Jetson Orin NX robot, meeting real-time requirements.

Conclusions:

  • YOLO-Crack offers a practical workflow for edge vision tasks, from geometric analysis to engineering verification.
  • The framework effectively addresses limitations of general detectors for mobile crack inspection.
  • Demonstrated successful real-time deployment and validation on a mobile robot platform.