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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Detection of Black Holes01:10

Detection of Black Holes

2.2K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.2K
Light Acquisition02:16

Light Acquisition

8.5K
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.
8.5K
Reducing Line Loss01:18

Reducing Line Loss

184
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
184
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
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

You might also read

Related Articles

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

Sort by
Same author

Organic base-promoted formal [4+1] cycloaddition of vinylethylene carbonates with elemental sulfur for facile access to thiophenones.

Chemical communications (Cambridge, England)·2026
Same author

Site-specific neoepitope induction by RNA editing reprograms tumor immunogenicity.

Frontiers in immunology·2026
Same author

Gasless single-port retroperitoneal laparoscopic adrenalectomy: a novel option for adrenal tumors.

Translational andrology and urology·2026
Same author

Clinical outcomes of a new modified tubeless cutaneous ureterostomy following radical cystectomy.

Translational andrology and urology·2026
Same author

Re-ascent triggered high-altitude pulmonary and cerebral edema in a Tibetan with pre-existing high-altitude polycythemia: a Case Report.

Frontiers in physiology·2026
Same author

Strain-invariant omnidirectional stretchable MXetronics.

Nature communications·2026

Related Experiment Video

Updated: Aug 4, 2025

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 lightweight ship target detection model based on improved YOLOv5s algorithm.

Yuanzhou Zheng1,2, Yuanfeng Zhang1,2, Long Qian1,2

  • 1School of Navigation, Wuhan University of Technology, Wuhan, PR China.

Plos One
|April 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces MC-YOLOv5s, a faster and more efficient ship detection algorithm. It significantly reduces model size and improves accuracy for enhanced navigation safety and ship supervision.

More Related Videos

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
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: Aug 4, 2025

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
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
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
  • Artificial Intelligence
  • Maritime Technology

Background:

  • Real-time ship detection is crucial for navigation safety and supervision.
  • Current models suffer from large parameters, high computation, and poor real-time performance, demanding significant memory and processing power.

Purpose of the Study:

  • To propose an efficient ship target detection algorithm, MC-YOLOv5s, addressing the limitations of existing models.
  • To improve detection speed and reduce computational complexity while maintaining high accuracy.

Main Methods:

  • Replaced the YOLOv5s backbone with the MobileNetV3-Small lightweight network for faster feature extraction.
  • Introduced a novel CNeB module, based on ConvNeXt-Block, to replace the original feature fusion module, enhancing spatial feature interaction and reducing model complexity.

Main Results:

  • MC-YOLOv5s reduced parameters by 6.98 MB compared to the original YOLOv5s.
  • Achieved an approximate 3.4% increase in mean Average Precision (mAP).
  • Demonstrated superior detection performance compared to other lightweight models.

Conclusions:

  • MC-YOLOv5s offers a significant improvement in speed and accuracy for ship detection.
  • The algorithm shows great application potential in ship visual inspection and maritime surveillance.
  • The developed code and models are publicly available for further research and application.