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

Light Acquisition02:16

Light Acquisition

9.9K
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.
9.9K

You might also read

Related Articles

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

Sort by
Same author

Oral Microbial and Metabolic Alterations in Patients With Oral Lichen Planus Concomitant With Type 2 Diabetes Mellitus.

MicrobiologyOpen·2026
Same author

Electroacupuncture-modulated DHCR24 facilitates spinal cord injury recovery by attenuating apoptosis and neuroinflammation via the Wnt signaling pathway.

Metabolic brain disease·2026
Same author

The effect of acute fatigue on somatosensory function differs between trained and untrained individuals.

Scientific reports·2026
Same author

Effects of Velocity-Based French Contrast Training on Lower-Limb Power and Delivery Kinetics in Medium-Fast Cricket Bowlers: A Randomized Controlled Trial.

Sports (Basel, Switzerland)·2026
Same author

Sex-specific impact of glycemic variability on long-term outcomes after acute myocardial infarction.

Scientific reports·2026
Same author

Improving placement of central venous catheters in 22 hospitals in China: a best practice implementation project.

JBI evidence implementation·2026

Related Experiment Video

Updated: Apr 16, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

8.9K

Adaptive adjacent context negotiation network for object detection in remote sensing imagery.

Yan Dong1,2, Yundong Liu2, Yuhua Cheng1

  • 1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.

Peerj. Computer Science
|August 15, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the Adaptive Adjacent Context Negotiation Network (A2CN-Net) to improve object detection in remote sensing images (RSIs). The novel network enhances accuracy for small objects and objects at various scales, achieving significant performance boosts.

Keywords:
Adjacent context negotiationGlobal to local aggregation enhancementObject detectionRemote sensing imagesSpectral context information

More Related Videos

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.8K
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

496

Related Experiment Videos

Last Updated: Apr 16, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

8.9K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.8K
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

496

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Accurate object localization in remote sensing images (RSIs) is crucial for applications like resource management and disaster response.
  • Existing methods face challenges with complex backgrounds, dense targets, scale variations, and small objects, leading to unsatisfactory detection accuracy.
  • The need for improved object detection algorithms in RSIs is driven by the increasing volume and complexity of available data.

Purpose of the Study:

  • To develop an advanced deep learning model for enhancing object detection accuracy in remote sensing images (RSIs).
  • To address the limitations of current methods in handling small objects, scale variations, and complex backgrounds.
  • To propose a novel network architecture that adaptively integrates multi-level features for robust object localization.

Main Methods:

  • Introduced the Adaptive Adjacent Context Negotiation Network (A2CN-Net) incorporating a composite fast Fourier convolution (CFFC) module to preserve small object information.
  • Employed a Global Context Information Enhancement (GCIE) module to capture and aggregate global spatial features for multi-scale object detection.
  • Developed a novel Adaptive Adjacent Context Negotiation (A2CN) network with adaptive feature fusion using learnable weights for local and adjacent branches.

Main Results:

  • The A2CN-Net demonstrated significant improvements in object detection performance on public datasets like DIOR and DOTA-v1.0.
  • Achieved a mean average precision (mAP) of 74.2% on the DIOR dataset.
  • Achieved a mean average precision (mAP) of 79.2% on the DOTA-v1.0 dataset, showcasing superior detection capabilities.

Conclusions:

  • The proposed A2CN-Net effectively enhances object detection accuracy in remote sensing images (RSIs) by addressing key challenges.
  • The network's adaptive feature integration and context negotiation mechanisms contribute to superior performance across various object scales and complexities.
  • A2CN-Net represents a significant advancement in remote sensing object detection, offering a more robust and accurate solution for practical applications.