Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Detection of Black Holes01:10

Detection of Black Holes

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...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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...
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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...
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

MRI findings in individuals with temporomandibular disorders.

Cranio : the journal of craniomandibular practice·2026
Same author

Phage-guided rapid enrichment of copper-protocatechuic acid nanoparticles for targeted antibacterial therapy.

Journal of nanobiotechnology·2026
Same author

Temporal-Spatial Differences of Nitrogen Source-Sink in Sediments of Wetland-River Connected System and Response Mechanism of Microbial Community Function.

Microorganisms·2026
Same author

Pancancer pro-angiogenic atlas unravels tumor-educated pericyte-augmented anti-angiogenic resistance.

Science bulletin·2026
Same author

Advances in PD-L1 Targeted Molecular Imaging Radiotracers Research: From Preclinical Exploration to Clinical Application.

Molecular imaging and biology·2026
Same author

Glucose-Responsive Dual-Enzyme Mimetic Nanoreactor Remodels Diabetic Periodontitis Microenvironment for Augmented Alveolar Bone Regeneration.

International journal of nanomedicine·2026

相关实验视频

Updated: May 26, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.3K

YOLO-DHGC:使用密集连接的双流结构进行小物体检测.

Lihua Chen1, Lumei Su1,2, Weihao Chen1

  • 1School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, China.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
概括

这项研究介绍了YOLO-DHGC,这是一种用于小物体检测的新方法. 它通过增强功能重复使用和专注于对象边界来实现高精度的缺陷检测.

关键词:
有密集的连接连接.小物体检测 小物体检测这是一个双流结构结构.

更多相关视频

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

471
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

369

相关实验视频

Last Updated: May 26, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

8.3K
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

471
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

369

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 小物体检测对于缺陷检测和医学成像等应用至关重要.
  • 由于小物体的特征有限和细节模糊,现有的方法难以达到较低的准确性.

研究的目的:

  • 提出一种有效的小型物体检测方法,YOLO-DHGC,以解决精度限制.
  • 通过改进特征提取和专注于形状边界来增强小物体的检测.

主要方法:

  • 推出了DenseHRNet,这是一个新的骨干网络,结合了密集的连接和高分辨率的功能地图,以增强功能重复使用和融合.
  • 设计了一个带有边缘门分支的双流结构,利用更高层次的信息来完善对对象边界和形态特征的关注.
  • 在公共和自建数据集上验证了YOLO-DHGC方法.

主要成果:

  • 在Market-PCB公共数据集上实现了96.3%的缺陷检测准确度.
  • 在工业应用中检测小物体缺陷方面表现出显著的有效性.
  • 提出的方法成功地捕捉了小物体的形态特征.

结论:

  • 通过增强特征表示和边界信息,YOLO-DHGC有效地提高了小物体检测的准确性.
  • 新型的DenseHRNet骨干和边缘门流有助于在具有挑战性的检测场景中提供卓越的性能.
  • 该方法显示了工业缺陷检测和其他小物体检测应用的巨大潜力.