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

You might also read

Related Articles

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

Sort by
Same author

Aristolochic Acid-Induced Nephrotoxicity: Molecular Mechanisms and Potential Protective Approaches.

International journal of molecular sciences·2020
Same author

Structural insights into immunoglobulin M.

Science (New York, N.Y.)·2020
Same author

Mutations of two FERONIA-like receptor genes enhance rice blast resistance without growth penalty.

Journal of experimental botany·2020
Same author

Receptor kinase FERONIA regulates flowering time in Arabidopsis.

BMC plant biology·2020
Same author

Dual-hybrid direct random phase approximation and second-order screened exchange with nonlocal van der Waals correlations for noncovalent interactions.

Journal of computational chemistry·2020
Same author

Genetic and functional analysis of two missense mutations in CD46 predispose to postpartum atypical hemolytic uremic syndrome.

Clinica chimica acta; international journal of clinical chemistry·2020

Related Experiment Video

Updated: Jul 14, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.6K

CAPN: a Combine Attention Partial Network for glove detection.

Feng Yu1,2, Jialong Zhu1, Yukun Chen1

  • 1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Jiangxia District, China.

Peerj. Computer Science
|October 9, 2023
PubMed
Summary

This study introduces a real-time glove detection algorithm using video surveillance to prevent accidents at electric power sites. The Combine Attention Partial Network (CAPN) achieves 96.59% average precision in detecting safety glove usage.

Keywords:
Attention mechanismChannel attentionObject detectionSpatial attentionTransfer learning

More Related Videos

Evaluation of Capnography Sampling Line Compatibility and Accuracy when Used with a Portable Capnography Monitor
07:51

Evaluation of Capnography Sampling Line Compatibility and Accuracy when Used with a Portable Capnography Monitor

Published on: September 29, 2020

8.9K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

569

Related Experiment Videos

Last Updated: Jul 14, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.6K
Evaluation of Capnography Sampling Line Compatibility and Accuracy when Used with a Portable Capnography Monitor
07:51

Evaluation of Capnography Sampling Line Compatibility and Accuracy when Used with a Portable Capnography Monitor

Published on: September 29, 2020

8.9K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

569

Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Safety glove non-compliance is a major cause of accidents at electric power sites.
  • Manual supervision is inefficient, and current methods lack effective supervision for glove detection.

Purpose of the Study:

  • To develop a real-time glove detection algorithm for enhancing safety at electric power operation sites.
  • To improve the accuracy of glove detection, especially with limited datasets.

Main Methods:

  • Proposed a Combine Attention Partial Network (CAPN) based on convolutional neural networks for accurate glove recognition.
  • Integrated channel and spatial attention modules to enhance feature extraction and recognition accuracy.
  • Utilized transfer learning to augment small glove datasets by transferring human hand features.

Main Results:

  • The proposed CAPN achieved a high average precision of 96.59% for glove detection.
  • Demonstrated the algorithm's effectiveness in real-time video surveillance scenarios.
  • Showcased significant improvement in detection accuracy compared to existing methods.

Conclusions:

  • The CAPN algorithm effectively addresses the challenge of accurate glove detection in electric power operations.
  • The integration of attention mechanisms and transfer learning significantly boosts detection performance.
  • This technology offers a promising solution for improving safety and reducing accidents caused by non-compliance with safety glove policies.