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

6.4K
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...
6.4K
Force Classification01:22

Force Classification

1.2K
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,...
1.2K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

103
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
103
Classification of Signals01:30

Classification of Signals

462
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
462
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
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.1K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
14.1K

You might also read

Related Articles

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

Sort by
Same author

A transformer and large-kernel convolution-based detection model for Red Turpentine Beetle infestation in pine trees.

Scientific reports·2026
Same author

Adaptive reconfiguration of prefrontal networks during prolonged cognitive interference: Evidence from fNIRS.

Brain research·2026
Same author

ESTGFormer: A spatio-temporal graph transformer with embedding and structure-aware loss for traffic forecasting.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

PGFD-YOLO: A dual-modal object detection framework with progressive gated fusion and foreground-guided distillation.

Journal of environmental management·2026
Same author

The Circadian Rhythm of Asthma Immune Metabolism.

Journal of immunology research·2026
Same author

A quantitative measurement of dichoptic color difference thresholds related to binocular luster across various luminance.

i-Perception·2026

Related Experiment Video

Updated: Jul 4, 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

542

HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection.

Lindong Tang1,2, Lijun Yun1,2, Zaiqing Chen1,2

  • 1College of Information, Yunnan Normal University, Kunming 650500, China.

Sensors (Basel, Switzerland)
|January 26, 2024
PubMed
Summary
This summary is machine-generated.

HRYNet improves object detection for autonomous driving in challenging conditions using a novel dual fusion pyramid and attention mechanism. This enhanced network, including a lightweight version, significantly outperforms YOLOv8s on multiple datasets.

Keywords:
DFGPNHRYNetLHRYNetRMAautonomous drivingobject detection

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

9.4K

Related Experiment Videos

Last Updated: Jul 4, 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

542
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

9.4K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Object detection is vital for autonomous driving perception.
  • Complex road environments (lighting, weather, density) degrade target visibility and cause feature loss in current networks.
  • Existing object detection networks struggle with feature extraction and fusion, compromising performance.

Purpose of the Study:

  • To introduce HRYNet, a novel methodology to enhance object detection in autonomous driving.
  • To address feature attenuation and loss caused by complex road scenarios.
  • To improve the learning capabilities of object detection networks for traffic targets.

Main Methods:

  • Developed a dual fusion gradual pyramid structure (DFGPN) for comprehensive multi-scale semantic information and improved feature layer connectivity.
  • Introduced a residual multi-head self-attention mechanism (RMA) for anti-interference feature extraction and enhanced target attention via channel weighting.
  • Evaluated HRYNet on BDD1000K, Visdrone, and a custom dataset, and developed Lightweight HRYNet (LHRYNet) for mobile optimization.

Main Results:

  • HRYNet achieved higher mAP_0.5 than YOLOv8s on BDD1000K (+10.8%), Visdrone (+16.7%), and custom dataset (+5.5%).
  • LHRYNet, optimized for mobile devices, reduced model parameters by 2 million.
  • LHRYNet outperformed YOLOv8s on the datasets with improvements of 6.7%, 10.9%, and 2.5% in mAP_0.5, respectively.

Conclusions:

  • HRYNet effectively enhances object detection in complex autonomous driving scenarios.
  • The proposed DFGPN and RMA modules significantly improve feature representation and reduce background interference.
  • LHRYNet offers a practical, efficient solution for mobile autonomous driving systems, maintaining superior detection performance.