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

Deconvolution01:20

Deconvolution

519
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
519
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

2.5K
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...
2.5K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

414
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...
414
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

509
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...
509
Buoyancy01:12

Buoyancy

12.1K
When an object is placed in a fluid, it either floats or sinks. All objects in a fluid experience a buoyant force. For example, a metal ball sinks, while a rubber ball floats. Similarly, a submarine can sink and float by adjusting its buoyancy.  The concept of buoyancy raises several interesting questions. For instance, where does this buoyant force come from? How much buoyant force is required to make an object sink or float? Do objects that sink get any support at all from the...
12.1K
Force Classification01:22

Force Classification

2.2K
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,...
2.2K

You might also read

Related Articles

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

Sort by
Same author

Application of Traditional Chinese Medicine in Alzheimer's Disease Treatment: A Focus on the Wnt/[Formula: see text]-Catenin Pathway.

The American journal of Chinese medicine·2025
Same author

Maintaining Drosha expression with Cdk5 inhibitors as a potential therapeutic strategy for early intervention after TBI.

Experimental & molecular medicine·2024
Same author

Exploring the mechanism of atherosclerosis and the intervention of traditional Chinese medicine combined with mesenchymal stem cells based on inflammatory targets.

Heliyon·2023
Same author

Amelioration of prediabetes-induced changes of dendritic structural plasticity.

Frontiers in bioscience (Landmark edition)·2018
Same author

TGF-beta/TGF-beta RII/CLC-3 axis promotes cognitive disorders in diabetes.

Frontiers in bioscience (Landmark edition)·2018
Same author

ClC-3 Expression and Its Association with Hyperglycemia Induced HT22 Hippocampal Neuronal Cell Apoptosis.

Journal of diabetes research·2016

Related Experiment Video

Updated: Jan 6, 2026

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

987

Optimized YOLOv8s framework with deformable convolution for underwater object detection.

Xin Wang1, Ke Li2, Feiyan Fan3

  • 1College of Computer Science and Software Engineering, Hohai University, Nanjing, 211100, China.

Scientific Reports
|November 27, 2025
PubMed
Summary

This study introduces O-YOLOv8s-DC, an optimized deep learning model for underwater object detection. It significantly improves the detection of small and occluded targets in challenging aquatic conditions.

Keywords:
Deep learningDeformable convolutionObject detectionUnderwater imageYOLOv8s

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.8K

Related Experiment Videos

Last Updated: Jan 6, 2026

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

987
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.8K

Area of Science:

  • Computer Vision
  • Marine Technology
  • Artificial Intelligence

Background:

  • Underwater object detection is crucial for the growing aquatic economy.
  • Challenges include small/occluded targets, varying object shapes, and poor image quality due to turbidity.

Purpose of the Study:

  • To develop an optimized deep learning framework, O-YOLOv8s-DC, for enhanced underwater object detection.
  • To address limitations of existing models in detecting small, occluded, and morphologically diverse underwater objects.

Main Methods:

  • Proposed O-YOLOv8s-DC framework integrating deformable convolution (C2f_DC), depth-weighted bidirectional feature pyramid (DeepBiFPN), content-aware feature reorganization (CARAFE), and efficient multi-scale attention (EMA).
  • Conducted ablation studies to validate individual module contributions.
  • Evaluated performance on LFIW and OI datasets.

Main Results:

  • O-YOLOv8s-DC significantly outperformed mainstream models like SSD, YOLOv8s, and DETR.
  • Achieved a higher AP@[0.50:0.05:0.95] compared to the original YOLOv8s.
  • Demonstrated enhanced performance for occluded targets at strict IoU thresholds (e.g., AP@0.75) and improved small-target recognition accuracy.

Conclusions:

  • O-YOLOv8s-DC provides reliable underwater object detection in complex environments.
  • Offers technical support for aquatic ecological protection and sustainable underwater operations.
  • The integrated modules effectively tackle challenges in underwater object detection.