Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Microcracking in Concrete

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Influenza forecasting method based on dual-chan nel feature fusion of VMD decomposition.

Scientific reports·2026

Same author

Point cloud generation adversarial network based on self-attention and curvature.

PloS one·2026

Same author

Chitosan-Based Composite Films Reinforced with Zein-Inulin-Thyme Essential Oil Pickering Emulsion for Enhanced Structural Integrity and Preservation Capacity.

Foods (Basel, Switzerland)·2026

Same author

Polysaccharides from pineapple peel: Structural characterization, film-forming properties and its effect on strawberry preservation.

International journal of biological macromolecules·2024

Same author

A human activity recognition method based on Vision Transformer.

Scientific reports·2024

Same author

Antiparasitic efficacy of flavonoids identified from Psoralea corylifolia against Tetrahymena piriformis in guppy (Poecilia reticulate).

Veterinary parasitology·2024

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 3, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Focusing on Cracks with Instance Normalization Wavelet Layer.

Lei Guo^1,2,3, Fengguang Xiong^1,2,3, Yaming Cao^1,2,3

¹Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China.

Sensors (Basel, Switzerland)

|January 11, 2025

Summary

This summary is machine-generated.

This study introduces a novel Instance Normalization Wavelet (INW) layer to improve automatic crack detection. The INW layer enhances feature extraction and noise filtering, leading to better recognition and faster model convergence for crack segmentation.

Keywords:

convolution neural networks crack detection feature fusion wavelet

More Related Videos

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

Related Experiment Videos

Last Updated: Jun 3, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Automatic crack detection is crucial for structural health monitoring but faces challenges due to complex crack topologies, diversity, and background noise.
Existing methods struggle with accurately segmenting thin, intricate crack patterns amidst noisy data.

Purpose of the Study:

To develop an advanced deep learning layer for enhanced automatic crack detection and segmentation.
To improve the accuracy, generalization capability, and convergence speed of crack detection models.

Main Methods:

Introduction of a novel Instance Normalization Wavelet (INW) layer, inspired by wavelet theory.
Embedding the INW layer into a deep learning model for crack segmentation tasks.
Incorporation of a fusion layer to integrate multi-layer information.

Main Results:

The INW layer effectively captures crack features while simultaneously filtering high-frequency noise.
Instance normalization within the INW layer mitigates feature differences, improving model generalization.
Experimental results on DeepCrack and CRACK500 datasets show steady enhancements in recognition and convergence performance.

Conclusions:

The proposed INW layer significantly improves automatic crack detection and segmentation performance.
The method offers a robust solution for challenging crack detection scenarios with complex features and noise.
The INW layer accelerates model training convergence and boosts overall recognition accuracy.