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

Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K
Light Acquisition02:16

Light Acquisition

8.5K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K
Methods of Classification and Identification01:28

Methods of Classification and Identification

37
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
37
Distance Measurements by Taping01:18

Distance Measurements by Taping

66
Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
66
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K
Vision01:24

Vision

53.6K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.6K

You might also read

Related Articles

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

Sort by
Same author

Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots.

Scientific data·2025
Same author

Detection of deterministic and chaotic signals on the basis of the LSTM model training results.

Chaos (Woodbury, N.Y.)·2025
Same author

Collision Avoidance Mechanism for Swarms of Drones.

Sensors (Basel, Switzerland)·2025
Same author

Creating Refined Datasets for Better Chaos Detection.

Sensors (Basel, Switzerland)·2025
Same author

Multimodal dataset for indoor 3D drone tracking.

Scientific data·2025
Same author

Intelligent Video Analytics for Human Action Recognition: The State of Knowledge.

Sensors (Basel, Switzerland)·2023
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

Related Experiment Video

Updated: Jul 20, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.3K

YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System.

Wojciech Lindenheim-Locher1, Adam Świtoński2,1, Tomasz Krzeszowski3,1

  • 1Polish-Japanese Academy of Information Technology, ul. Koszykowa 86, 02-008 Warsaw, Poland.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study precisely detects drones using YOLOv5 on multi-camera images, improving 3D drone tracking. A new evaluation metric, the average centroid distance, enhances detection performance analysis.

Keywords:
ViconYOLOdeep learningdrone detectiondrone localizationmotion captureunmanned aerial vehicle

More Related Videos

Visually Mediated Odor Tracking During Flight in Drosophila
08:50

Visually Mediated Odor Tracking During Flight in Drosophila

Published on: January 26, 2009

10.0K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K

Related Experiment Videos

Last Updated: Jul 20, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.3K
Visually Mediated Odor Tracking During Flight in Drosophila
08:50

Visually Mediated Odor Tracking During Flight in Drosophila

Published on: January 26, 2009

10.0K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Accurate 3D drone tracking is crucial for applications like surveillance and autonomous navigation.
  • Existing detection methods often struggle with precision in complex, multi-camera environments.

Purpose of the Study:

  • To develop and evaluate a precise drone detection method for the preliminary stage of 3D drone tracking challenges.
  • To introduce a novel evaluation metric for assessing 3D drone detection performance.

Main Methods:

  • Training and testing the YOLOv5 deep learning network on real, multimodal data, including synchronized video and motion capture data.
  • Utilizing an asymmetric cross with markers for precise 3D position and orientation determination.
  • Incorporating synthetic data from the AirSim simulation platform for robust training and testing.

Main Results:

  • Demonstrated the effectiveness of YOLOv5 for drone detection in synchronized multi-camera systems.
  • Proposed and validated a new metric (average distance between centroids) for evaluating 3D detection accuracy.
  • Achieved promising results in drone localization using marker-based cross detection.

Conclusions:

  • The YOLOv5 network shows significant potential for precise drone detection in 3D tracking scenarios.
  • The proposed evaluation metric offers a more adequate assessment of detection performance in 3D.
  • Combining real and simulated data enhances the robustness of drone detection models.