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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

125
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
125
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

2.0K
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...
2.0K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

449
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
449
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

154
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
154
Methods of Classification and Identification01:28

Methods of Classification and Identification

187
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...
187
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

529
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
529

You might also read

Related Articles

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

Sort by
Same author

Aggregation of high-frequency RBD mutations of SARS-CoV-2 with three VOCs did not cause significant antigenic drift.

Journal of medical virology·2022
Same author

Emerging nanomedicines of paclitaxel for cancer treatment.

Journal of controlled release : official journal of the Controlled Release Society·2022
Same author

Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies.

Nature·2022
Same author

Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project.

The Lancet. Planetary health·2022
Same author

Improved intergranular corrosion resistance of Al-Mg-Mn alloys with Sc and Zr additions.

Micron (Oxford, England : 1993)·2022
Same author

The effect of using personal-level indoor air cleaners and respirators on biomarkers of cardiorespiratory health: a systematic review.

Environment international·2022

Related Experiment Video

Updated: Sep 12, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.9K

YOLO11-YX: An efficient algorithm for marine debris target detection.

Yuxin Wang1, Shuo Liu2, Yansong He1

  • 1School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.

Marine Pollution Bulletin
|August 8, 2025
PubMed
Summary

This study introduces YOLO11-YX, an advanced algorithm for automated marine debris detection. It significantly improves accuracy in complex environments, offering a robust solution for environmental monitoring.

Keywords:
Feature fusionMarine debris detectionObject detectionYOLO11-YX

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

3.9K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.4K

Related Experiment Videos

Last Updated: Sep 12, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.9K
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

3.9K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.4K

Area of Science:

  • Environmental Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Marine debris pollution is a growing global concern.
  • Automated detection systems are crucial for effective monitoring and management.
  • Existing object detection methods struggle with accuracy and robustness in complex marine environments, especially for small debris.

Purpose of the Study:

  • To develop an enhanced algorithm for more accurate and robust automated detection of marine debris.
  • To address the limitations of traditional object detection methods in complex backgrounds and with small objects.

Main Methods:

  • Introduction of YOLO11-YX, an enhanced algorithm based on YOLO11s.
  • Integration of three novel modules: SDown (downsampling), C3SE (feature extraction), and FAN (feature fusion).
  • SDown module preserves details during dimensionality reduction.
  • C3SE module refines convolutional structure with SENet for improved detection in complex settings.
  • FAN module enhances detail and contextual recognition for small targets.

Main Results:

  • YOLO11-YX achieved a 2.44% improvement in marine debris detection accuracy compared to YOLO11s.
  • The new algorithm reached an overall accuracy rate of 62.32%.
  • Demonstrated enhanced performance in handling complex backgrounds and detecting small marine debris objects.

Conclusions:

  • YOLO11-YX provides a potent and dependable solution for automated marine debris detection.
  • The enhanced algorithm offers significant improvements in accuracy and robustness.
  • The developed technology has broad applicability in environmental monitoring and pollution control efforts.