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

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

350
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
350
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Dynamic Equilibrium02:20

Dynamic Equilibrium

62.9K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
62.9K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.8K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.1K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.1K
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Flexible Black-Phase CsPbI<sub>3</sub> Photodetectors Enabled by Low-Temperature Processing via BA<sub>2</sub>MA<sub>2</sub>Pb<sub>3</sub>I<sub>10</sub> Structural Templating.

ACS applied materials & interfaces·2026
Same author

Analysis of macroscopic cracks in triple cation perovskite films fabricated by the anisole antisolvent method.

The Journal of chemical physics·2025
Same author

U<sup>2</sup>-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting.

Microorganisms·2024
Same author

Research on system of ultra-flat carrying robot based on improved PSO algorithm.

Frontiers in neurorobotics·2023
Same author

Integrated strategy for widely targeted metabolome characterization of Peucedani Radix.

Journal of chromatography. A·2022
Same author

Simultaneous determination of eight tryptic peptides in musk using high-performance liquid chromatography coupled with tandem mass spectrometry.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2021

Related Experiment Video

Updated: Feb 6, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.8K

Dynamic bidirectional data recomposition for efficient road garbage segmentation in semi-supervised learning.

Suheng Peng1, Jiacai Liao2, Libo Cao1

  • 1School of Mechanical and Vehicle Engineering, Hunan University, Lushan South Road, Yuelu District, Changsha, Hunan Province, Changsha, 410082, Hunan, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 4, 2026
PubMed
Summary

This study introduces Dynamic Bidirectional Data Recomposition (DBDR) to improve road garbage segmentation using semi-supervised learning (SSL). DBDR enhances model performance by dynamically balancing labeled and unlabeled data, overcoming annotation limitations in urban waste management.

Keywords:
Deep learningGarbage segmentationSemantic segmentationSemi-supervised learning

More Related Videos

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.3K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K

Related Experiment Videos

Last Updated: Feb 6, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.8K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.3K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Deep neural networks (DNNs) are effective for road garbage segmentation but require expensive pixel-level annotations.
  • Balancing annotation costs and segmentation accuracy is crucial for efficient urban waste management.
  • Semi-supervised learning (SSL) leverages unlabeled data to reduce annotation dependency, but struggles with data diversity under extreme annotation imbalance.

Purpose of the Study:

  • To address representation stagnation in SSL for road garbage segmentation caused by scarce, non-diverse labeled data.
  • To introduce a novel mechanism, Dynamic Bidirectional Data Recomposition (DBDR), to enhance SSL performance by optimizing data interaction.
  • To improve the economic feasibility of smart city waste management technologies.

Main Methods:

  • Developed the Dynamic Bidirectional Data Recomposition (DBDR) mechanism for SSL.
  • Integrated labeled data into unlabeled streams based on confidence levels during early training.
  • Utilized a dynamic memory queue and dual validation for knowledge transfer from unlabeled to labeled data during mid-training.
  • Ensured DBDR's compatibility with existing mainstream SSL frameworks.

Main Results:

  • DBDR significantly boosted performance on a real-world road garbage dataset compared to five state-of-the-art baseline models.
  • Ablation experiments confirmed DBDR's effectiveness in segmenting challenging, visually similar targets like plastic and paper.
  • The method demonstrated improved exploitation of data information and overcame model performance stagnation.

Conclusions:

  • DBDR effectively solves representation stagnation in SSL under extreme annotation imbalance.
  • The proposed mechanism offers a significant performance improvement for road garbage segmentation.
  • DBDR presents an economically viable solution for advancing smart city waste management infrastructure.