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

The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

56.1K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
56.1K
Introduction to Special Senses01:26

Introduction to Special Senses

7.6K
Sensory receptors play an integral part in comprehending our external and internal environments. They receive diverse stimuli, converting them into the nervous system's electrochemical signals. This conversion occurs as the stimulus alters the sensory neuron's cell membrane potential, instigating the generation of an action potential. This action potential is subsequently transmitted to the central nervous system (CNS), which integrates with other sensory data or higher cognitive...
7.6K
Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

667
Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
667
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

843
In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
For instance, imagine a point A on a rigid body engaged in circular motion. The translational velocity of this particular point can be calculated by taking the time derivatives of the displacement equation, which essentially measures the...
843
Tactile and Chemical Senses01:27

Tactile and Chemical Senses

806
Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
806
Rotation of Asymmetric Top01:11

Rotation of Asymmetric Top

1.6K
By definition, a spherically symmetric body has the same moment of inertia about any axis passing through its center of mass. This situation changes if there is no spherical symmetry. Since most rigid bodies are not spherically symmetric, these require special treatment.
The relationship between the angular momentum of any rigid body and its angular velocity, both of which are vectors, involves the moment of inertia. The moment of inertia is a scalar quantity only for spherically symmetric...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Quantitative acetylated proteomics on left atrial appendage tissues revealed atrial energy metabolism and contraction status in patients with valvular heart disease with atrial fibrillation.

Frontiers in cardiovascular medicine·2022
Same author

Ion-Migration Mechanism: An Overall Understanding of Anionic Redox Activity in Metal Oxide Cathodes of Li/Na-Ion Batteries.

Advanced materials (Deerfield Beach, Fla.)·2022
Same author

Self-supervised knowledge distillation for complementary label learning.

Neural networks : the official journal of the International Neural Network Society·2022
Same author

HECTD3 regulates the tumourigenesis of glioblastoma by polyubiquitinating PARP1 and activating EGFR signalling pathway.

British journal of cancer·2022
Same author

Finite Element Analysis of Elastoplastic Elements in the Iwan Model of Bolted Joints.

Materials (Basel, Switzerland)·2022
Same author

FTO/RUNX2 signaling axis promotes cementoblast differentiation under normal and inflammatory condition.

Biochimica et biophysica acta. Molecular cell research·2022

Related Experiment Video

Updated: Feb 6, 2026

Femtosecond Laser Filaments for Use in Sub-Diffraction-Limited Imaging and Remote Sensing
06:16

Femtosecond Laser Filaments for Use in Sub-Diffraction-Limited Imaging and Remote Sensing

Published on: April 25, 2019

8.0K

Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images.

Shuxin Li1, Zhilong Zhang2, Biao Li3

  • 1ATR National Key Laboratory, National University of Defense Technology, Changsha 410073, China. lishuxin@nudt.edu.cn.

Sensors (Basel, Switzerland)
|August 22, 2018
PubMed
Summary

This study introduces a deep learning algorithm for ship detection in remote sensing images. It effectively identifies ships in complex backgrounds using multiscale rotated bounding boxes, improving both detection accuracy and orientation estimation.

Keywords:
deep learningmultiscale rotated bounding boxremote sensing imageship detection

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.6K
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

603

Related Experiment Videos

Last Updated: Feb 6, 2026

Femtosecond Laser Filaments for Use in Sub-Diffraction-Limited Imaging and Remote Sensing
06:16

Femtosecond Laser Filaments for Use in Sub-Diffraction-Limited Imaging and Remote Sensing

Published on: April 25, 2019

8.0K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

1.6K
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

603

Area of Science:

  • Computer Vision
  • Remote Sensing Technology
  • Artificial Intelligence

Background:

  • Remote sensing images present challenges for ship detection due to arbitrary target orientations and background clutter.
  • Horizontal bounding boxes often include irrelevant background information, complicating precise localization, especially near shores or with closely spaced vessels.

Purpose of the Study:

  • To develop a deep learning algorithm for accurate ship target detection in complex remote sensing backgrounds.
  • To enable precise localization and orientation estimation of ship targets using multiscale rotated bounding boxes.

Main Methods:

  • A deep learning approach utilizing multiscale rotated bounding boxes for ship detection.
  • Feature extraction via a pretrained deep network with two output paths: one for classification, another for location and angle prediction.
  • Training involves matching prior multiscale rotated bounding boxes to ground-truth data, optimizing sample selection for efficiency.

Main Results:

  • The algorithm demonstrates robustness in detecting ships amidst wave clutter, close proximity, shoreline proximity, and varying scales.
  • Experimental results on a remote sensing dataset confirm superior performance compared to existing algorithms.
  • The method accurately provides both the location and orientation of detected ship targets.

Conclusions:

  • The proposed deep learning algorithm effectively addresses challenges in remote sensing ship detection.
  • Multiscale rotated bounding boxes are crucial for accurate localization and orientation estimation in complex environments.
  • This approach offers enhanced performance and detailed information for ship surveillance and analysis.