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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

115
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...
115
Segregation in Fresh Concrete01:16

Segregation in Fresh Concrete

108
Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
108
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

140
Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
140

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A semantic segmentation model for road cracks combining channel-space convolution and frequency feature aggregation.

Mingxing Zhang1, Jian Xu2

  • 1School of Electronics and information, Xi'an Polytechnic University, Xi'an, 710048, China. zmx18792354440@163.com.

Scientific Reports
|July 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new semantic segmentation model to accurately detect road cracks using combined channel-spatial convolution and frequency features. The enhanced model improves road safety by precisely identifying cracks in complex backgrounds.

Keywords:
Deep learningFrequency feature aggregationImage segmentation, Neural networksRoad crack detection

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Image Processing

Background:

  • Road cracks pose significant safety risks in transportation infrastructure.
  • Existing semantic segmentation models struggle with crack detection due to challenges in capturing spatial-channel relationships and complex backgrounds.

Purpose of the Study:

  • To propose an advanced semantic segmentation model for accurate road crack detection.
  • To address limitations in current models regarding spatial-channel feature coupling and background differentiation.

Main Methods:

  • Developed a novel convolutional block integrating channel-spatial convolution for enhanced pixel identification.
  • Introduced a frequency domain feature aggregation module to improve crack edge contrast.
  • Incorporated a feature refinement module to boost segmentation accuracy.

Main Results:

  • The proposed model demonstrates superior performance compared to popular general-purpose models.
  • Experimental results validate the model's effectiveness in identifying road cracks.

Conclusions:

  • The novel semantic segmentation model offers improved accuracy and application potential for road crack detection.
  • This research contributes to enhancing road safety through advanced image analysis techniques.