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 Concept Videos

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.7K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.7K

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

Related Experiment Video

Updated: Dec 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

879

A Robust Fabric Defect Detection Method Based on Improved RefineDet.

Huosheng Xie1, Zesen Wu1

  • 1School of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China.

Sensors (Basel, Switzerland)
|August 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved RefineDet model for robust fabric defect detection. The enhanced model offers accurate defect localization across various fabric patterns, improving quality control.

Keywords:
Bottom-up path augmentation Transfer Connection BlockDIoU-NMSFull Convolutional Channel Attention blockcosine annealing schedulerfabric defect detectionimproved RefineDetobject detection

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.0K

Related Experiment Videos

Last Updated: Dec 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

879
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.0K

Area of Science:

  • Computer Vision
  • Materials Science
  • Artificial Intelligence

Background:

  • Automated fabric defect detection is crucial for maintaining textile quality.
  • Existing methods may struggle with diverse defect types and complex fabric patterns.
  • Object detection models offer potential for precise localization of fabric flaws.

Purpose of the Study:

  • To develop a robust and accurate fabric defect detection method.
  • To enhance the localization accuracy of defect detection using an improved object detection model.
  • To validate the effectiveness of the proposed method on various fabric types.

Main Methods:

  • Utilized an improved RefineDet model as the base architecture.
  • Incorporated a novel head structure with Full Convolutional Channel Attention (FCCA) and Bottom-up Path Augmentation Transfer Connection Block (BA-TCB).
  • Applied optimization techniques including attention mechanisms, DIoU-NMS, and cosine annealing scheduler.

Main Results:

  • The proposed method demonstrated robust performance in fabric defect detection.
  • Achieved improved defect localization accuracy compared to baseline models.
  • Effectively detected defects in fabric images with unpatterned, regular, and irregular patterns.

Conclusions:

  • The improved RefineDet-based method is highly suitable for fabric defect detection tasks.
  • The integration of FCCA and BA-TCB significantly enhances localization precision.
  • The method provides a reliable solution for automated textile quality inspection.