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: Nov 7, 2025

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

744

Pedestrian Detection Using Multispectral Images and a Deep Neural Network.

Jason Nataprawira1, Yanlei Gu1, Igor Goncharenko1

  • 1College of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.

Sensors (Basel, Switzerland)
|April 30, 2021
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

Contactless Respiratory Waveform Estimation Using a Depth Camera and AI-Based Body Detection.

Sensors (Basel, Switzerland)·2026
Same author

Lymph node metastasis from a small papillary renal cell carcinoma mimicking pancreatic infiltration: A case report.

Urology case reports·2026
Same author

Delayed Clinical Manifestation of Malignant Potential in a Cortisol-Secreting Adrenal Tumor Initially Diagnosed as Adenoma.

IJU case reports·2026
Same author

Fatal Interstitial Pneumonitis During Combined Degarelix, Darolutamide, and Docetaxel Therapy for Metastatic Prostate Cancer.

Cureus·2026
Same author

Robot-Assisted Partial Nephrectomy via a Retroperitoneal Approach With Selective Arterial Clamping Guided by Blood Flow Analysis in a Horseshoe Kidney: A Case Report.

IJU case reports·2026
Same author

Complete Response to Pembrolizumab After Progression on Avelumab Maintenance in Metastatic Urothelial Carcinoma.

IJU case reports·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)·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

Multispectral imaging and deep neural networks significantly improve pedestrian detection in vehicles, especially at night. This advancement enhances safety by boosting accuracy and reducing processing time in Advanced Driver-Assistance Systems (ADAS).

Area of Science:

  • Computer Vision
  • Automotive Safety
  • Artificial Intelligence

Background:

  • Vehicle-pedestrian crashes are a major cause of fatalities and injuries.
  • Current Advanced Driver-Assistance Systems (ADAS) and autonomous vehicle pedestrian detection systems struggle with performance reduction in low-light conditions.
  • Reliable pedestrian detection is crucial regardless of ambient lighting.

Purpose of the Study:

  • To evaluate pedestrian detection performance across various lighting conditions.
  • To propose the use of multispectral imaging and optimized deep neural networks to enhance detection accuracy.
  • To improve the reliability and efficiency of pedestrian detection systems.

Main Methods:

  • Comparative analysis of pedestrian detection using RGB, thermal, and multispectral image formats.
Keywords:
deep neural networkdifferent lighting conditionsmultispectral imagespedestrian detectionprocessing time

More Related Videos

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K

Related Experiment Videos

Last Updated: Nov 7, 2025

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

744
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K
  • Development and optimization of deep neural network architectures for pedestrian detection.
  • Evaluation of detection accuracy and processing time trade-offs.

    • Main Results:

    • Multispectral images demonstrated superior performance for pedestrian detection in diverse lighting conditions compared to RGB and thermal.
    • The proposed deep neural network achieved a 6.9% improvement in pedestrian detection accuracy over baseline methods.
    • Optimizations reduced processing time by 22.76% with a minimal 2% decrease in detection accuracy.

    Conclusions:

    • Multispectral imaging is the optimal solution for robust pedestrian detection across varying lighting conditions.
    • The developed deep neural network effectively enhances pedestrian detection accuracy and efficiency for ADAS.
    • Balancing processing time and detection accuracy is feasible through network optimization.