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

90
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...
90
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

1.9K
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...
1.9K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

98
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...
98
Deconvolution01:20

Deconvolution

193
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...
193
Force Classification01:22

Force Classification

1.3K
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,...
1.3K
Observational Learning01:12

Observational Learning

213
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
213

You might also read

Related Articles

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

Sort by
Same author

KerSpecGen: Co-piloting formal Kernel specification synthesis with refined knowledge graphs and large language models.

PloS one·2025
Same author

WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm.

Bioengineering (Basel, Switzerland)·2023
Same author

An Instance Segmentation Model Based on Deep Learning for Intelligent Diagnosis of Uterine Myomas in MRI.

Diagnostics (Basel, Switzerland)·2023
Same author

Effects of Five Prebiotics on Growth, Antioxidant Capacity, Non-Specific Immunity, Stress Resistance, and Disease Resistance of Juvenile Hybrid Grouper (<i>Epinephelus fuscoguttatus</i> ♀ × <i>Epinephelus lanceolatus</i> ♂).

Animals : an open access journal from MDPI·2023
Same author

Management of take-all disease caused by <i>Gaeumannomyces graminis</i> var. <i>tritici</i> in wheat through <i>Bacillus subtilis</i> strains.

Frontiers in microbiology·2023
Same author

Enriched Environment Attenuates Ferroptosis after Cerebral Ischemia/Reperfusion Injury via the HIF-1<i>α</i>-ACSL4 Pathway.

Oxidative medicine and cellular longevity·2023

Related Experiment Video

Updated: Jul 23, 2025

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

581

UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection.

Haixia Pan1, Jiahua Lan1, Hongqiang Wang1

  • 1School of Software, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
Summary

This study introduces UWV-Yolox, an enhanced object detection model for underwater videos. It improves accuracy on blurry, low-contrast footage by incorporating frame context and attention mechanisms.

Keywords:
coordinate attentionframe-level optimizationloss functionobject detectionunderwater video

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

9.5K

Related Experiment Videos

Last Updated: Jul 23, 2025

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

581
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

9.5K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Underwater video object detection faces challenges due to poor video quality (blurriness, low contrast).
  • Existing Yolo models struggle with these conditions and lack frame-to-frame contextual analysis.

Purpose of the Study:

  • To develop an improved video object detection model, UWV-Yolox, for challenging underwater environments.
  • To enhance object detection accuracy and stability in low-quality underwater videos.

Main Methods:

  • Utilized Contrast Limited Adaptive Histogram Equalization for video augmentation.
  • Introduced a CSP_CA module with Coordinate Attention to improve object representation.
  • Developed a novel loss function (regression and jitter loss).
  • Implemented a frame-level optimization module leveraging inter-frame relationships.

Main Results:

  • The UWV-Yolox model achieved a mAP@0.5 of 89.0% on the UVODD dataset.
  • Demonstrated a 3.2% improvement over the original Yolox model.
  • Exhibited more stable object predictions compared to other models.

Conclusions:

  • UWV-Yolox effectively addresses the limitations of existing models for underwater object detection.
  • The proposed enhancements, including attention mechanisms and frame-level optimization, significantly boost performance.
  • The model's improvements are adaptable to other object detection architectures.