Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

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.

Force Classification

Force Classification

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,...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

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...

You might also read

Related Articles

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

Sort by

Same author

Terahertz polarization sensing with a four-level cesium Rydberg-atom system.

Optics express·2026

Same author

Responsive NIR fluorescent probes targeting PKM2 with enzyme-activating properties for precision cancer diagnosis.

Biosensors & bioelectronics·2026

Same author

PMEPA1 modulates YAP1 nuclear translocation to disrupt EMT subtypes and promote metastasis in Biliary tract cancer.

Cell death & disease·2026

Same author

Red CPL from water insoluble achiral carbon dots assisted by cyclodextrin MOF templating.

Chemical communications (Cambridge, England)·2026

Same author

Quasi-zero stiffness suspension for reducing the resonance frequency of underwater electrodynamic transducers.

The Journal of the Acoustical Society of America·2026

Same author

Global Fast Terminal Sliding Mode Control for Trajectory Tracking Control of Quadrotor UAVs.

Sensors (Basel, Switzerland)·2025

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

Search research articles

Related Experiment Video

Updated: Oct 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images.

Xuanye Li¹, Hongguang Li², Yalong Jiang²

¹School of Electrical and Information Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

This summary is machine-generated.

This study introduces a lightweight object detection framework for Unmanned Aerial Vehicles (UAVs). The new model significantly reduces computational cost while improving detection accuracy for real-time drone applications.

Keywords:

UAV images lightweight convolutional neural network object detection

More Related Videos

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

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

Related Experiment Videos

Last Updated: Oct 22, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

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

Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Unmanned Aerial Vehicles (UAVs) require efficient real-time object detection for enhanced flexibility and intelligence.
Existing object detection models for UAVs often have high computational burdens, hindering deployment on mobile platforms.
Challenges include complex ground scenes, small object sizes, and high object density in UAV imagery.

Purpose of the Study:

To develop a lightweight object detection framework for UAVs.
To improve detection accuracy and efficiency for real-time applications on drones.
To reduce the computational cost of object detection models for mobile platforms.

Main Methods:

Proposed an anchor-free, lightweight object detection framework.
Utilized a lightweight backbone and a simultaneous up-sampling and detection module.
Incorporated an objectness branch to aid multi-class center point prediction.

Main Results:

Achieved a 92.78% reduction in computational cost compared to CenterNet with ResNet18.
Improved mean Average Precision (mAP) by 2.8 points on the Visdrone-2018-VID dataset.
Reached a frame rate of approximately 220 FPS, demonstrating high efficiency.

Conclusions:

The proposed lightweight framework enhances object detection accuracy and efficiency for UAVs.
The method offers a significant reduction in computational resources with minimal performance compromise.
Validated through ablation studies and comparison with other lightweight methods on UAV datasets.