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関連する概念動画

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

5.4K
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...
5.4K
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...
2.6K
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...
2.8K

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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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複雑な環境における赤外線船舶標的検出アルゴリズム 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
まとめ
この要約は機械生成です。

この研究では,赤外線船舶検出のための高度なアルゴリズムであるPEW_YOLOv8を紹介しています. 複雑な環境でのミスと誤った検出を大幅に削減し,小さな標的の精度を向上させます.

キーワード:
ディープラーニングとは,ディープラーニングです.赤外線画像は赤外線で撮影したものです.ターゲット認識 ターゲット認識YOLOv8 でした.

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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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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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科学分野:

  • コンピュータビジョン コンピュータビジョン
  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • リモートセンシング (リモートセンサー)

背景:

  • 船舶の赤外線検出は,騒音,遮蔽,不明瞭な小さなターゲットなどの課題に直面し,高度のミスおよび誤った検出率につながります.
  • 既存のアルゴリズムは複雑な環境で苦労しており,船舶を正確に識別するための改善された方法が必要です.

研究 の 目的:

  • 複雑な赤外線環境でのパフォーマンスを向上させるために,YOLOv8をベースにした,強化された船舶標的検出アルゴリズム,PEW_YOLOv8を提案する.
  • 現在の赤外線船検知方法の限界に対処するため,特に小さなターゲットと困難な環境条件に関するものです.

主な方法:

  • FFA-Netを使用して画像の事前処理を行い,コントラストとクリアリティを高めます.
  • 小規模ターゲットの特徴表現を改善するために,マルチパスの融合を備えた新しいPGIG-Backboneネットワーク.
  • 騒音を抑制し,標的の識別性を向上させるための強化されたマルチスケール注意ネックネットワーク (EMA-Neck).
  • WIoU Lossの統合により,閉塞や重複の処理がより良くなります.

主要な成果:

  • PEW_YOLOv8アルゴリズムは,Raytron Technologyの赤外線船データセットで92.2%の検出精度を達成しました.
  • 標準のYOLOv8.8.1と比較して,平均精度 (mAP50) の3.9%とmAP50:95の3.1%の改善が実証されました.
  • 小さいターゲットの詳細表現能力を向上させ,背景騒音に対する識別能力を向上させました.

結論:

  • PEW_YOLOv8は,船舶の赤外線検出において,複雑なシナリオにおいて標準のYOLOv8を上回る,重要な進歩を提供します.
  • 提案された方法は,騒音,遮断,および小さなターゲットによって引き起こされる課題を効果的に対処し,より堅牢で正確な検出につながります.
  • このアルゴリズムは,困難な赤外線画像条件下での信頼性の高い船舶モニタリングを必要とするアプリケーションに希望を示しています.