Jove
Visualize
Contact Us

Related Concept Videos

Binary Fission01:26

Binary Fission

2.6K
Binary fission is the primary mode of asexual reproduction in prokaryotes, such as bacteria. It results in the production of two genetically identical daughter cells. This highly efficient process ensures the rapid propagation of bacterial populations under favorable conditions and involves coordinated cellular and molecular events.DNA Replication and SeparationThe process begins with the replication of the bacterial chromosome. The circular DNA molecule unwinds at a specific origin of...
2.6K
Binary Fission01:20

Binary Fission

63.0K
Fission is the division of a single entity into two or more parts, which regenerate into separate entities that resemble the original. Organisms in the Archaea and Bacteria domains reproduce using binary fission, in which a parent cell splits into two parts that can each grow to the size of the original parent cell. This asexual method of reproduction produces cells that are all genetically identical.
63.0K
Introduction to Membrane Traffic01:44

Introduction to Membrane Traffic

9.4K
The ER, Golgi apparatus, endosomes, and lysosomes work in tandem to modify, sort, and package proteins and lipids. An integrated membrane trafficking network facilitates the back and forth shuttling of molecules within different organelles in the same cell or across the cell membrane.
The transport of soluble and membrane proteins is mediated by transport vesicles that collect cargo from one cellular compartment and deliver it to another by fusing with the target organelle membrane. The Rab...
9.4K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K

You might also read

Related Articles

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

Sort by
Same author

A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes.

Sensors (Basel, Switzerland)·2021
Same author

Color Image Generation from Range and Reflection Data of LiDAR.

Sensors (Basel, Switzerland)·2020
Same author

Color Image Generation from LiDAR Reflection Data by Using Selected Connection UNET.

Sensors (Basel, Switzerland)·2020
Same author

Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation.

Sensors (Basel, Switzerland)·2019
Same author

Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults.

Sensors (Basel, Switzerland)·2018
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: Jan 26, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

2.8K

Traffic Light Recognition Based on Binary Semantic Segmentation Network.

Hyun-Koo Kim1, Kook-Yeol Yoo2, Ju H Park3

  • 1Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38544, Korea. kim-hk@ynu.ac.kr.

Sensors (Basel, Switzerland)
|April 13, 2019
PubMed
Summary

This study introduces an efficient deep-learning method for traffic light recognition, crucial for autonomous vehicles. The novel approach enhances recognition performance while significantly reducing computational demands.

Keywords:
advanced driver assistance systemartificial neural networksbinary semantic segmentationdeep learningtraffic light detectiontraffic light recognition

More Related Videos

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
08:17

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

11.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Related Experiment Videos

Last Updated: Jan 26, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

2.8K
A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
08:17

A Semantic Priming Event-related Potential ERP Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

11.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Traffic light recognition is essential for advanced driving assistance systems (ADAS) and autonomous vehicles (AVs).
  • Existing methods often involve computationally intensive region proposal and regression, limiting real-time application.
  • Accurate detection of small objects like traffic lights remains a challenge in complex driving environments.

Purpose of the Study:

  • To propose a novel, efficient two-staged deep-learning method for traffic light recognition.
  • To improve recognition performance and reduce computational complexity compared to conventional methods.
  • To provide a guideline for detecting and recognizing small objects in computer vision applications.

Main Methods:

  • A two-staged approach combining pixel-wise semantic segmentation and a fully convolutional network (FCN).
  • Utilized a binary-semantic segmentation network for efficient candidate detection of small objects.
  • Employed connected components labeling for bounding box generation, avoiding complex region proposal algorithms.

Main Results:

  • The proposed method demonstrated superior recognition performance over conventional two-staged object detection techniques.
  • Significantly reduced computational complexity and hardware requirements, making it suitable for real-time applications.
  • The FCN with initial 1x1 convolution filters effectively classified traffic light colors.

Conclusions:

  • The developed deep-learning framework offers an effective and efficient solution for traffic light recognition.
  • This approach provides a valuable network design strategy for the detection and recognition of various small objects.
  • The method contributes to the advancement of safer and more reliable autonomous driving systems.