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

Force Classification01:22

Force Classification

1.6K
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,...
1.6K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

445
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...
445
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

527
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
527
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

392
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
392
Detection of Black Holes01:10

Detection of Black Holes

2.3K
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...
2.3K
Personal Protective Equipment01:20

Personal Protective Equipment

1.8K
Personal protective equipment (PPE) is unique clothing or equipment worn by an employee to minimize or prevent exposure to infectious agents. PPE creates a barrier between the employee and the infectious materials. PPE must be readily available in the patient care area. PPE includes gloves, gowns and aprons, masks and respirators, goggles, face shields, shoes, and headcovers:
1.8K

You might also read

Related Articles

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

Sort by
Same author

Irisin Protects Musculoskeletal Homeostasis via a Mitochondrial Quality Control Mechanism.

International journal of molecular sciences·2024
Same author

PDHX acetylation facilitates tumor progression by disrupting PDC assembly and activating lactylation-mediated gene expression.

Protein & cell·2024
Same author

Effect and mechanism of Lycium barbarum polysaccharide on gastrointestinal motility in slow transit constipation.

Naunyn-Schmiedeberg's archives of pharmacology·2024
Same author

A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer.

Heliyon·2024
Same author

Efficacy and Safety of C3 Laminectomy Combined with Open-Door Laminoplasty versus Open-Door Laminoplasty Alone: A Systematic Review and Meta-Analysis.

World neurosurgery·2024
Same author

Optimized personalized management approach for moderate/severe OHSS: development and prospective validation of an OHSS risk assessment index.

Human reproduction (Oxford, England)·2024

Related Experiment Video

Updated: Sep 6, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

A lightweight YOLOv3 algorithm used for safety helmet detection.

Lixia Deng1, Hongquan Li2, Haiying Liu2

  • 1School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, Shandong Province, China. AmandaDeng084@126.com.

Scientific Reports
|June 29, 2022
PubMed
Summary

This study introduces a mini and lightweight YOLOv3 (ML-YOLOv3) object detection algorithm. ML-YOLOv3 significantly reduces computational costs and parameter size compared to YOLOv3, enhancing detection efficiency.

More Related Videos

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
07:30

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

Published on: September 21, 2017

9.0K
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: Sep 6, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
07:30

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

Published on: September 21, 2017

9.0K
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
  • Deep Learning
  • Object Detection

Background:

  • YOLOv3 is a widely used object detection algorithm but suffers from large computational costs and parameter sizes.
  • The complexity of YOLOv3 limits its application in resource-constrained environments.

Purpose of the Study:

  • To develop a lightweight and efficient object detection algorithm based on YOLOv3.
  • To reduce the floating point operations (FLOPs) and parameter size of YOLOv3 while maintaining detection accuracy.

Main Methods:

  • Integration of Cross Stage Partial Network (CSPNet) and GhostNet to create an efficient residual network (CSP-Ghost-Resnet).
  • Design of a novel backbone network (ML-Darknet) by combining CSPNet and Darknet53 for gradient diversion.
  • Development of a lightweight multiscale feature extraction network (PAN-CSP-Network).

Main Results:

  • The proposed mini and lightweight YOLOv3 (ML-YOLOv3) achieved 29.7% of YOLOv3's FLOPs and 29.4% of its parameter size on a helmet dataset.
  • ML-YOLOv3 demonstrated superior performance in terms of computational cost and detection effectiveness compared to YOLO5.

Conclusions:

  • The developed ML-YOLOv3 offers a significant reduction in computational complexity and model size.
  • ML-YOLOv3 presents a viable lightweight alternative for object detection tasks, outperforming existing models like YOLO5 in efficiency and accuracy.