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相关概念视频

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.6K
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...
8.6K
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.0K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
2.0K
IR Spectrum01:19

IR Spectrum

2.4K
When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
2.4K
Detection of Black Holes01:10

Detection of Black Holes

2.6K
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...
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IR Spectrometers01:25

IR Spectrometers

2.8K
There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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相关实验视频

Updated: Feb 25, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

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在复杂的环境中使用红外船舶目标检测算法 PEW_YOLOv8

Tingkai Dong1, Menglin Zhu2, Gaofeng Tang3

  • 1School of Software, Henan University of Engineering, Zhengzhou, 451191, Henan, China.

Scientific reports
|February 23, 2026
PubMed
概括
此摘要是机器生成的。

这项研究介绍了PEW_YOLOv8,这是用于红外船舶检测的先进算法. 它大大减少了复杂环境中的错误和错误检测,提高了小目标的准确性.

关键词:
深度学习是一种深度学习.红外图像中的红外图像.目标识别 目标识别这就是YOLOv8的意义.

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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

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相关实验视频

Last Updated: Feb 25, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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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
  • 遥感 遥感 遥感 遥感

背景情况:

  • 红外船舶检测面临诸如噪音,遮蔽和模糊的小目标等挑战,导致高错误和错误检测率.
  • 现有的算法难以应对复杂的环境,因此需要改进用于准确识别船舶的方法.

研究的目的:

  • 提出基于YOLOv8的增强型船舶目标检测算法PEW_YOLOv8,以提高复杂红外环境中的性能.
  • 解决当前红外船舶检测方法的局限性,特别是关于小目标和具有挑战性的环境条件.

主要方法:

  • 使用FFA-Net进行图像预处理,以提高对比度和清晰度.
  • 一个新的PGIG-Backbone网络,具有多路径融合,用于改进小目标特征表达.
  • 一个增强的多尺度注意力部网络 (EMA-Neck) 抑制噪音并提高目标区分能力.
  • 集成WIoU Loss以更好地处理堵塞和重叠.

主要成果:

  • PEW_YOLOv8算法在Raytron技术的红外船舶数据集上实现了92.2%的检测准确度.
  • 与标准YOLOv8.8.5相比,平均平均精度 (mAP50) 提高了3.9%,mAP50:95提高了3.1%.
  • 成功提高了小型目标的细节表达能力,并改善了对背景噪声的辨别能力.

结论:

  • PEW_YOLOv8在红外船舶检测方面取得了重大进展,在复杂的场景中表现优于标准YOLOv8.
  • 提出的方法有效地解决了噪音,阻塞和小目标所带来的挑战,从而实现更强大,更准确的检测.
  • 这种算法对在具有挑战性的红外成像条件下需要可靠的船舶监控的应用具有前景.