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

Reducing Line Loss01:18

Reducing Line Loss

155
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...
155
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

342
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
342
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

406
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...
406
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Discovery of (2<i>R</i>,4<i>R</i>)-4-((<i>S</i>)-2-Amino-3-methylbutanamido)-2-(4-boronobutyl)pyrrolidine-2-carboxylic Acid (AZD0011), an Actively Transported Prodrug of a Potent Arginase Inhibitor to Treat Cancer.

Journal of medicinal chemistry·2024
Same author

Design and Synthesis of Acyclic Boronic Acid Arginase Inhibitors.

Journal of medicinal chemistry·2024
Same author

CRISPR-Cas12a-regulated DNA adsorption on MoS<sub>2</sub> quantum dots: Enhanced enzyme mimics for sensitive colorimetric detection of human monkeypox virus and human papillomavirus DNA.

Talanta·2024
Same author

3D printing incorporating gold nanozymes with mesenchymal stem cell-derived hepatic spheroids for acute liver failure treatment.

Biomaterials·2024
Same author

Subglottic airway injury during fiberscope-monitored intubation with a supraglottic airway device: A randomized controlled comparison of three tracheal tubes.

Chinese medical journal·2024
Same author

Effects of filling substrates on remediation performance and sulfur transformation of sulfate reducing packed-bed bioreactors treating acid mine drainage.

Journal of environmental management·2024

Related Experiment Video

Updated: Jul 10, 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

559

Keypoint regression strategy and angle loss based YOLO for object detection.

Xiuling Wang1, Lingkun Kong1, Zhiguo Zhang1

  • 1College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China.

Scientific Reports
|November 18, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces KR-AL-YOLO, an enhanced YOLOv4 model that improves small object detection accuracy. By incorporating keypoint regression and angle loss, it achieves superior performance over standard YOLOv4.

More Related Videos

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

9.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

Related Experiment Videos

Last Updated: Jul 10, 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

559
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

9.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • YOLOv4 is popular for real-time object detection but struggles with small objects due to rigid bounding box regression.
  • Existing methods fail to fully utilize object asymmetry, limiting detection accuracy.

Purpose of the Study:

  • To enhance YOLOv4's object detection capabilities, particularly for small objects.
  • To introduce a novel approach, KR-AL-YOLO, addressing YOLOv4's limitations.

Main Methods:

  • Developed KR-AL-YOLO (keypoint regression strategy and angle loss based YOLOv4).
  • Integrated a keypoint regression strategy and an angle-loss function.
  • Employed an improved feature fusion technique for better information flow.

Main Results:

  • KR-AL-YOLO achieved an average precision of 45.6% on the COCO2017 dataset.
  • Demonstrated superior performance compared to YOLOv4 and other one-stage detectors.
  • Validated the effectiveness of keypoint regression and feature fusion for accuracy.

Conclusions:

  • KR-AL-YOLO significantly improves object detection accuracy, especially for small objects.
  • The proposed keypoint regression and angle loss modules enhance localization precision.
  • Enhanced feature fusion further boosts the model's overall performance.