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

5.2K
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...
5.2K
Detection of Black Holes01:10

Detection of Black Holes

2.1K
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...
2.1K
Reducing Line Loss01:18

Reducing Line Loss

140
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...
140
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

26
Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
26
Leaky Scanning02:28

Leaky Scanning

5.0K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.0K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

55
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...
55

You might also read

Related Articles

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

Sort by
Same author

Human Circadian Rhythm Through the Lens of GIScience: A Systematic Review.

Research square·2026
Same author

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Age- and sex-specific normative values for reticulocyte indices and their relation to early-stage CKM syndrome.

BMC cardiovascular disorders·2026
Same author

Comparison of early and late drainage interventions in necrotizing pancreatitis: a systematic review and meta-analysis.

BMC gastroenterology·2026
Same author

SeqPE: Transformer with Sequential Position Encoding.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Tick-derived protein Qinghaienin-based anticoagulant coating inhibits activation of the zymogen FXII.

International journal of biological macromolecules·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 21, 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

448

CLRNetV2: A Faster and Stronger Lane Detector.

Tu Zheng, Yifei Huang, Yang Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 18, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel network for intelligent vehicle vision navigation, enhancing lane detection by integrating high-level semantic and low-level features. The method accurately identifies complex lane structures, improving localization and overall system performance.

    More Related Videos

    Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
    11:41

    Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

    Published on: February 1, 2020

    20.3K
    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
    11:54

    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

    Published on: March 13, 2017

    9.2K

    Related Experiment Videos

    Last Updated: May 21, 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

    448
    Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
    11:41

    Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

    Published on: February 1, 2020

    20.3K
    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
    11:54

    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

    Published on: March 13, 2017

    9.2K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Intelligent Transportation Systems

    Background:

    • Accurate lane detection is crucial for intelligent vehicle navigation systems.
    • Current methods struggle with complex lane geometries (e.g., Y-shapes) and underutilize multi-level features.
    • Integrating semantic and local features remains an under-explored area for robust lane detection.

    Purpose of the Study:

    • To develop a lane detection method that effectively utilizes both high-level semantic and low-level features.
    • To improve the accuracy of lane detection, especially for complex and dense lane scenarios.
    • To enhance the localization precision of detected lanes.

    Main Methods:

    • Proposed the Cross Layer Refinement Network (CLRN) integrating semantic and local feature levels.
    • Introduced Fast-ROIGather for enhanced global context gathering.
    • Developed the Correlation Discrimination Module (CDM) for accurate dense lane prediction.
    • Implemented LineIoU loss for whole-unit lane regression and improved localization.

    Main Results:

    • The CLRN significantly outperforms existing state-of-the-art lane detection methods.
    • Demonstrated superior performance in detecting complex and dense lane structures.
    • Achieved improved localization accuracy through the novel network design and loss function.

    Conclusions:

    • The proposed Cross Layer Refinement Network effectively leverages multi-level features for enhanced lane detection.
    • The integration of semantic and local features, along with specialized modules, addresses limitations in current lane detection techniques.
    • This approach offers a significant advancement for robust and accurate lane detection in intelligent vehicles.