Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 21, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
00:05

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

Published on: September 29, 2019

8.2K

ICDW-YOLO: An Efficient Timber Construction Crack Detection Algorithm.

Jieyang Zhou1, Jing Ning2, Zhiyang Xiang1

  • 1College of Computer Science and Engineering, Jishou University, Jishou 416000, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

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
Lumber Defects01:23

Lumber Defects

110
Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
110
Prismatic Beams: Problem Solving01:15

Prismatic Beams: Problem Solving

110
In the design of a supported timber beam subjected to a distributed load, both the beam's physical dimensions and the timber's characteristics, such as its grade and species, are critical. These factors determine the allowable stress values, which are crucial for calculating the necessary beam depth to ensure structural integrity and safety.
The design begins with analyzing the beam as a free body to identify moments and force balances, thereby determining support reactions. Next, the...
110
Lumber01:19

Lumber

110
Lumber is derived from logs which are harvested, debarked, and processed into long pieces with a rectangular cross-section. The transformation of logs into lumber involves multiple steps, beginning with an automated saw that slices the log into slabs. These slabs are then transported via a conveyor belt to smaller saws, where they are cut into square-edged pieces of specific widths.
Initially, the surfaces of these lumber pieces are rough, and their dimensions may vary slightly from one end to...
110
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
Structural Properties and Dimensions of Lumber01:21

Structural Properties and Dimensions of Lumber

82
Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
The strength characteristics of...
82

You might also read

Related Articles

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

Sort by
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

This study introduces ICDW-YOLO, an improved crack detection algorithm for wood materials. The enhanced YOLOv8 model effectively identifies small cracks, improving structural safety and production quality.

Area of Science:

  • Computer Vision
  • Materials Science
  • Structural Health Monitoring

Background:

  • Accurate crack detection in wood is crucial for safety and quality control.
  • Existing algorithms struggle with scale variations and data quality in wood crack detection.

Purpose of the Study:

  • To develop an improved crack detection algorithm for wooden materials.
  • To enhance the YOLOv8 model for better recognition of small and varied cracks.

Main Methods:

  • Proposed ICDW-YOLO (improved crack detection for wooden material-YOLO) based on YOLOv8.
  • Introduced novel neck network, layer structure (GSConv, GS bottleneck), and anchor algorithm with dual-layer attention.
  • Implemented a gather-distribute mechanism and a higher-resolution input layer.
Keywords:
crack detectionneural networkobject detection

More Related Videos

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.2K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.0K

Related Experiment Videos

Last Updated: Jun 21, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
00:05

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

Published on: September 29, 2019

8.2K
Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.2K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.0K

Main Results:

  • ICDW-YOLO achieved a mAP50-95 of 79.018% on a custom wood crack dataset, a 1.869% improvement over YOLOv8.
  • Demonstrated robust generalization on fire/smoke, aerial remote sensing, and coco128 datasets.
  • Achieved competitive mAP50 (69.226%) and mAP50-95 (44.210%) on diverse datasets.

Conclusions:

  • The proposed ICDW-YOLO algorithm effectively detects cracks in wooden materials with improved accuracy and small target sensitivity.
  • The enhancements offer better feature fusion and recognition without significant complexity increase.
  • ICDW-YOLO shows strong generalization capabilities across various detection tasks.