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 Experiment Video

Updated: Dec 3, 2025

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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.2K

SoC FPGA Accelerated Sub-Optimized Binary Fully Convolutional Neural Network for Robotic Floor Region Segmentation.

Chi-Chia Sun1,2, Afaroj Ahamad1, Pin-He Liu1

  • 1Digital System Design Lab, National Formosa University, Huwei 632, Taiwan.

Sensors (Basel, Switzerland)
|October 31, 2020
PubMed
Summary
This summary is machine-generated.

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

The effect of lactic acid bacteria fermentation on physicochemical properties of starch from fermented proso millet flour.

Food chemistry·2023
Same author

Heterogeneous Fusion of Camera and mmWave Radar Sensor of Optimizing Convolutional Neural Networks for Parking Meter System.

Sensors (Basel, Switzerland)·2023
Same author

Antioxidant activity and hepatoprotective effect of 10 medicinal herbs on CCl<sub>4</sub>-induced liver injury in mice.

World journal of gastroenterology·2020
Same author

Influence of Grape Berry Maturity on Juice and Base Wine Composition and Foaming Properties of Sparkling Wines from the Champagne Region.

Molecules (Basel, Switzerland)·2018
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)·2026
Same journal

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

Sensors (Basel, Switzerland)·2026
Same journal

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

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

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

Sensors (Basel, Switzerland)·2026
Same journal

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

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

A new Binary Fully Convolutional Neural Network (B-FCN) precisely segments robotic floor regions for improved robot navigation. This efficient method accelerates real-time computation on embedded platforms, enhancing path planning capabilities.

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Accurate segmentation of floor regions is crucial for robot navigation in complex indoor environments.
  • Existing methods may struggle with real-time performance on embedded systems.
  • Efficient visual perception is key for autonomous robot operation.

Purpose of the Study:

  • To propose a novel Binary Fully Convolutional Neural Network (B-FCN) for precise robotic floor region segmentation.
  • To optimize the B-FCN using the Taguchi method for enhanced accuracy.
  • To accelerate the B-FCN for real-time computation on embedded platforms.

Main Methods:

  • Development of a Binary Fully Convolutional Neural Network (B-FCN).
  • Application of the Taguchi method for sub-optimization of the B-FCN architecture.
Keywords:
Binary Neural Network (BNN)Fully Convolutional Network (FCN)SoC-FPGATaguchi Method (TM)UGVfloor segmentationmotion control

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

853

Related Experiment Videos

Last Updated: Dec 3, 2025

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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.2K
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

853
  • Utilization of a PYNQ FPGA platform with heterogeneous computing for acceleration.
  • Training the model with 6000 datasets for improved accuracy and convergence.

    • Main Results:

    • Achieved an average segmentation accuracy of 84.80% for robotic floor regions.
    • Demonstrated efficient reduction in BRAM size (0.5-1%) through FPGA synthesis.
    • High GOPS/W (Giga Operations Per Second per Watt) achieved, indicating power efficiency.
    • Enabled real-time computation suitable for embedded robotic vision.

    Conclusions:

    • The proposed B-FCN offers precise floor region segmentation for complex indoor environments.
    • The accelerated architecture is ideal for low-power embedded devices, benefiting robot navigation, route planning, and motion planning.
    • The methodology enhances robot vision capabilities, enabling better path searching and shortest path problem solutions.