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相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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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...
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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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相关实验视频

Updated: Jan 10, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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平衡的X射线安全数据集和增强的YOLO用于非法货物检测.

Songlin Zhang1, Dingju Zhu2,3, KaiLeung Yung4

  • 1School of Artificial Intelligence, South China Normal University, Foshan, 528225, China. 2024024560@m.scnu.edu.cn.

Scientific data
|November 26, 2025
PubMed
概括

本研究介绍了一个平衡的X射线偷运物品检测数据集和两个新型模型,ASEA-Net和CSEC-Net. 这些进步显著提高了检测准确性和安全查模型效率.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 安全技术 安全技术

背景情况:

  • 现有的X射线走私物品检测数据集存在严重的阶级不平衡和有限的高质量注释数据.
  • 模型对复杂场景的适应性差,阻碍了在现实应用中有效检测走私物品.

研究的目的:

  • 构建一个平衡的X射线走私物品检测数据集,解决阶级不平衡和数据稀缺问题.
  • 开发适应复杂场景的轻量级和高精度检测模型.

主要方法:

  • 通过结合SIXray和PIDray数据集,创建了12个走私品类别中的13,728张图像的平衡数据集.
  • 使用类特定增强框架 (CSAF) 和随机低样本采集来实现统一的类分布.
  • 两种改进的基于YOLOv11s的模型,ASEA-Net和CSEC-Net,被提议用于增强检测.

主要成果:

  • 亚洲网络实现了95.78%的准确性和93.55%的mAP@50,在更少的参数下优于YOLOv11.
  • CSEC-Net显著减少了参数和FLOP,使其能够在资源有限的边缘设备上部署.
  • 这两种模型在复杂的场景中都表现出了强的表现.

结论:

  • 均衡的数据集有效地解决了阶级不平衡,并加强了X射线走私物品检测模型培训.
  • 亚洲网络和CSEC网络为安全查应用提供了更高的准确性,效率和适应性.
  • 拟议的模型验证了在先进的走私物品检测系统中平衡数据集和新型架构的价值.