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

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

200
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
200
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

1.2K
In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
1.2K
Center of Gravity00:58

Center of Gravity

4.9K
The center of gravity (COG) of an object is the point where the object's total weight is considered to be concentrated. Knowing the location of the center of gravity is useful when predicting the behavior of a moving object or designing static structures. In a uniform gravitational field, the center of gravity is similar to the center of mass (COM); yet, these two points can be positioned differently. For example, the Moon's center of mass lies very close to its geometric center, but...
4.9K
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

25
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
25
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

7.2K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.2K
Finding the Center of Gravity01:03

Finding the Center of Gravity

3.4K
The center of gravity of a body is an imaginary point where the body's total weight is assumed to be concentrated, and the body is perfectly balanced. The center of the mass of a body is a point at which the whole of the mass of the body appears to be concentrated. If the acceleration due to gravity, g, has the same value at all points on a body, its center of gravity is identical to its center of mass. The center of gravity of homogeneous bodies such as a sphere, cube, or rectangular plate...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Astrocyte elevated gene-1 induces breast cancer proliferation and invasion through upregulating HER2/neu expression.

Chinese medical journal·2012
Same author

Inhibition of proliferation, viability, migration and invasion of gastric cancer cells by Aurora-A deletion.

Asian Pacific journal of cancer prevention : APJCP·2012
Same author

Repression of PDGF-R-α after cellular injury involves TNF-α, formation of a c-Fos-YY1 complex, and negative regulation by HDAC.

American journal of physiology. Cell physiology·2012
Same author

Single-stage posterior debridement and single-level instrumented fusion for spontaneous infectious spondylodiscitis of the lumbar spine.

Acta orthopaedica Belgica·2012
Same author

Cancer prevention health services research: an emerging field.

Journal of cancer education : the official journal of the American Association for Cancer Education·2012
Same author

[Assessment of cochlear implant performance with Mandarin Hearing In Noise Test].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2012

Related Experiment Video

Updated: May 28, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K

Lightweight UAV Landing Model Based on Visual Positioning.

Ning Zhang1, Junnan Tan1, Kaichun Yan1

  • 1School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary

This study introduces Land-YOLO, a lightweight algorithm for precise unmanned aerial vehicle (UAV) landings. It enhances detection accuracy and reduces model size for mobile deployment.

Keywords:
BiFPN structureYOLOv8aerial photographbidirectional characteristic pyramidlanding signlightweight

More Related Videos

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

1.3K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K

Related Experiment Videos

Last Updated: May 28, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K
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

1.3K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.2K

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Precise unmanned aerial vehicle (UAV) landings are critical for various applications.
  • Deploying complex computer vision models on resource-constrained UAV terminals remains a challenge.

Purpose of the Study:

  • To develop a lightweight algorithm for accurate UAV landing sign detection.
  • To optimize the model for efficient deployment on mobile UAV terminals.

Main Methods:

  • Proposed Land-YOLO algorithm based on YOLOv8n, incorporating GhostConv and PartialConv for network lightening.
  • Enhanced feature fusion using the bidirectional feature pyramid network (BiFPN) module.
  • Utilized real and virtual environment datasets for training and evaluation.

Main Results:

  • Land-YOLO achieved 1.4% higher precision and 0.91% higher mAP0.5 compared to the YOLOv8n baseline.
  • Reduced model memory usage by 42.8% and floating-point operations per second (FLOPs) by 32.4%.
  • Demonstrated suitability for mobile terminal deployment on UAVs.

Conclusions:

  • The Land-YOLO algorithm significantly improves landing sign detection precision and efficiency for UAVs.
  • The lightweight design makes the model highly suitable for real-time applications on embedded UAV systems.
  • This advancement facilitates more reliable and accessible automated UAV landing operations.