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

Leveraging Transformer-GNN Integration for Multilingual News Speech-to-Text Similarity Modeling.

Big data·2026
Same author

Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers.

Cancer biotherapy & radiopharmaceuticals·2025
Same author

Medical LLMs: Fine-Tuning vs. Retrieval-Augmented Generation.

Bioengineering (Basel, Switzerland)·2025
Same author

Leveraging Shapley Additive Explanations for Feature Selection in Ensemble Models for Diabetes Prediction.

Bioengineering (Basel, Switzerland)·2025
Same author

UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging.

Cancers·2024
Same author

Ensemble Classification Model With CFS-IGWO-Based Feature Selection for Cancer Detection Using Microarray Data.

International journal of telemedicine and applications·2024
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: The Design of Adolescents' Physical Health Prediction System Based on Deep Reinforcement Learning.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: The Navigation of Mobile Robot in the Indoor Dynamic Unknown Environment Based on Decision Tree Algorithm.

Computational intelligence and neuroscience·2025
See all related articles
  1. Home
  3. Intelligent Semantic Segmentation For Self-driving Vehicles Using Deep Learning.
  1. Home
  3. Intelligent Semantic Segmentation For Self-driving Vehicles Using Deep Learning.

Related Experiment Video

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K

Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning.

Qusay Sellat1, SukantKishoro Bisoy1, Rojalina Priyadarshini1

  • 1Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, India.

Computational Intelligence and Neuroscience
|January 27, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an effective deep learning approach for semantic segmentation in self-driving cars. The model enhances real-time visual processing for improved scenario comprehension and technology acceptance.

More Related Videos

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

672
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

Related Experiment Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K
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

672
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Accurate real-time visual signal processing is crucial for self-driving systems.
  • Semantic segmentation enables pixelwise scene understanding, vital for autonomous vehicle safety and acceptance.
  • Deep learning advancements are essential for achieving the necessary processing speed and accuracy.

Purpose of the Study:

  • To present an effective deep learning approach for semantic segmentation in autonomous vehicles.
  • To improve real-time visual processing for enhanced scenario comprehension.
  • To validate the proposed model against existing benchmarks.

Main Methods:

  • Utilized a combination of deep learning architectures: Convolutional Neural Networks (CNNs) and Autoencoders.
  • Incorporated advanced techniques such as Feature Pyramid Networks and Bottleneck Residual Blocks.
  • Trained and tested the model on the augmented CamVid dataset.

    • Main Results:

    • The developed model demonstrates effective semantic segmentation capabilities for autonomous driving scenarios.
    • Achieved competitive performance metrics compared to baseline models in the literature.
    • The approach addresses the challenges of intricate pixel interactions in camera data.

    Conclusions:

    • The proposed deep learning model offers a promising solution for semantic segmentation in self-driving cars.
    • This advancement contributes to the reliability and safety of autonomous driving technology.
    • Further validation and comparison with state-of-the-art methods are recommended.