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...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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

Structural basis of cooperative neutralization of the PldB toxin by immunity proteins in Pseudomonas aeruginosa.

Communications biology·2026
Same author

Metformin Sensitizes HR+/HER2- Breast Cancer Cells to CDK4/6 Inhibitor via Suppressing of PI3K/AKT/mTOR Signaling Pathway.

Drug design, development and therapy·2026
Same author

Single-cell long-read profiling of olfactory sensory neuron differentiation and diversity.

Communications biology·2026
Same author

Retraction: MALAT1 predicts poor survival in osteosarcoma patients and promotes cell metastasis through associating with EZH2.

Oncotarget·2026
Same author

The bidirectional mechanistic links between Alzheimer's disease and cardiovascular disease.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2026
Same author

Single-cell screening of indigenous hydrocarbon-degrading bacteria for efficient controlling the groundwater hydrocarbon pollution of operating industrial.

Journal of hazardous materials·2026

相关实验视频

Updated: Jul 11, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

CCDN-DETR:一种基于受约束对比度否定的检测变压器,用于多类合成孔径雷达对象检测.

Lei Zhang1, Jiachun Zheng1, Chaopeng Li1

  • 1School of Ocean Information Engineering, Jimei University, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
概括

这项研究介绍了CCDN-DETR,这是一种新的合成孔径雷达 (SAR) 对象检测模型. 它通过利用变压器架构显著提高了船舶目标识别和多类检测准确度.

关键词:
这就是为什么SAR SAR SAR.深度学习是一种深度学习.检测变压器的检测变压器对象检测检测对象检测对象检测

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

530
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

1.4K

相关实验视频

Last Updated: Jul 11, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
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

530
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

1.4K

科学领域:

  • 遥感 遥感 遥感 遥感
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 卷积神经网络 (CNN) 是有效的SAR对象检测,特别是船舶目标.
  • 将变压器结构集成到SAR探测器中可以增强目标定位,但现有的方法无法充分利用自我注意的远程建模.
  • 多类SAR目标检测仍然是一个研究有限的领域.

研究的目的:

  • 提出一种基于检测变压器 (DETR) 框架的新型SAR探测器CCDN-DETR.
  • 解决现有的SAR探测器的局限性,包括充分利用自我注意力和改进多类检测.
  • 为了适应基于变压器的探测器对SAR数据的多尺度特征.

主要方法:

  • 开发了CCDN-DETR,这是一个基于检测变压器 (DETR) 框架的SAR检测器.
  • 引入交叉尺度编码器,以在SAR数据中模拟和融合不同尺度的信息.
  • 优化解码器输入,采用IOU损失对象查询初始化,并结合受约束的对比性拒绝训练.

主要成果:

  • 在SSDD,HRSID和SAR-AIRcraft数据集的组合中,CCDN-DETR实现了91.9%的平均平均精度 (mAP).
  • 在多类MSAR数据集上表现出强的表现,mAP为83.7%,优于基于CNN的模型.
  • 提出的方法提高了模型的融合速度,并改善了各种SAR目标类别的检测.

结论:

  • CCDN-DETR有效地利用变压器架构来改进SAR对象检测,特别是在多类场景中.
  • 跨尺度编码器和优化查询选择方案的集成解决了SAR数据的多尺度性质.
  • 这项研究通过提供更强大,更准确的检测模型来推进SAR目标识别.