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

Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...

You might also read

Related Articles

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

Sort by
Same author

Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.

Research squareĀ·2026
Same author

Semi-Supervised Traffic Sign Detection with Dynamic Pseudo-Label Selection and Gated Feature Fusion-Based Proposal Refinement.

Sensors (Basel, Switzerland)Ā·2026
Same author

Targeting octopamine synthesis genes LsTDC and LsTĪ²H impairs reproduction, feeding and virus transmission in the small brown planthopper Laodelphax striatellus.

Pest management scienceĀ·2026
Same author

Organic Electrochemical Transformations of Resource Small Molecules.

Accounts of chemical researchĀ·2026
Same author

Electrochemical hydroxylation of alkenes with H<sub>2</sub>O.

National science reviewĀ·2026
Same author

Discovery of Peripherally Restricted Indazole-Based Phosphodiesterase 4B Inhibitors Targeting the C-terminal Regulatory Helix for Treating Acute Lung Injury.

Journal of medicinal chemistryĀ·2026
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)Ā·2026
Same journal

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

Sensors (Basel, Switzerland)Ā·2026
Same journal

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

Sensors (Basel, Switzerland)Ā·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)Ā·2026
Same journal

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

Sensors (Basel, Switzerland)Ā·2026
Same journal

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

Sensors (Basel, Switzerland)Ā·2026
See all related articles

Related Experiment Video

Updated: May 11, 2026

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

1.2K

Semi-Supervised Traffic Sign Detection with Dual Confidence Fusion Module and Structured Block-Regularized Neck.

Chenhui Xia1, Yeqin Shao1, Meiqin Che1

  • 1School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new semi-supervised learning framework for reliable traffic sign detection in autonomous driving. The method enhances pseudo-labeling and feature representation, significantly improving detection accuracy with limited labeled data.

Keywords:
Dual Confidence Fusion ModuleSpatial-Context-Aware UpsamplingStructured Block-Regularized Neck networkpseudo-label optimizationsemi-supervised learningtraffic sign detection

Related Experiment Videos

Last Updated: May 11, 2026

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

1.2K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Autonomous Driving Systems

Background:

  • Traffic sign detection is crucial for autonomous driving safety.
  • Manual data annotation for training is resource-intensive.
  • Semi-supervised learning (SSL) offers a viable alternative but faces challenges with pseudo-label reliability and accuracy.

Purpose of the Study:

  • To develop a novel SSL framework to improve traffic sign detection accuracy.
  • To address limitations in pseudo-label filtering and feature representation in existing SSL methods.

Main Methods:

  • Proposed a framework integrating a Dual Confidence Fusion (DC-Fusion) module for reliable pseudo-labeling.
  • Introduced a Structured Block-Regularized Neck (SBR-Neck) for optimized feature representation.
  • Incorporated Spatial-Context-Aware Upsampling (SCA-Upsampling) within SBR-Neck to preserve spatial details.

Main Results:

  • Achieved mAP50 scores of 10.4% (1% labeled data), 17.8% (2%), 23.7% (5%), and 32.1% (10%).
  • Outperformed the 'Efficient Teacher' baseline by 3.07% to 11% across different labeled data percentages.
  • Demonstrated robust detection performance in complex traffic scenarios.

Conclusions:

  • The proposed DC-Fusion and SBR-Neck framework significantly enhances traffic sign detection using SSL.
  • The method effectively improves pseudo-label reliability and feature representation, leading to superior accuracy.
  • This framework offers a robust solution for autonomous driving systems requiring accurate traffic sign recognition with minimal labeled data.