Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Parallel Processing01:20

Parallel Processing

224
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
224
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
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...
7.1K
Visual System01:26

Visual System

684
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
684

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Fructus xanthii Extract Alleviates Osteoarthritis by Preserving Cartilage Integrity.

Journal of visualized experiments : JoVE·2026
Same author

A Multi-Dimensional Vision-Based System for External Thread Defect Detection with Integrated Security Defense.

Sensors (Basel, Switzerland)·2026
Same author

Single-cell multiomic and spatial landscape of the primate pineal gland reveals circadian and melatonin regulatory architecture.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

iGABASnFR2 is an improved genetically encoded protein sensor of GABA.

eLife·2026
Same author

GDEIM-SF: A Lightweight UAV Detection Framework Coupling Dehazing and Low-Light Enhancement.

Sensors (Basel, Switzerland)·2026
Same author

<i>Pseudomonas aeruginosa</i> SG01: A Novel Polyethylene-Degrading Bacterium in Petrochemical Wastewater.

Polymers·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

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

635

複雑な交通場面のための軽量な多段階の視覚検出方法

Xuanyi Zhao1, Xiaohan Dou1, Jihong Zheng2

  • 1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,複雑な交通状況の 強力な視覚検出フレームワークを導入し, 霧や弱光によって劣化した画像を 強化します. このシステムは,車両と歩行者の検出の精度を向上させ,インテリジェントな交通システムのための実用的な解決策を提供します.

キーワード:
イメージデハージングインテリジェント・トラフィック・監視弱光の強化オブジェクト検出

さらに関連する動画

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

関連する実験動画

Last Updated: Sep 9, 2025

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

635
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

科学分野:

  • コンピュータ・ビジョン
  • 人工知能
  • インテリジェントな輸送システム

背景:

  • 交通シーンでの画像の劣化 (霧,低照明,遮蔽) はオブジェクト検出の性能を阻害します.
  • 既存のシステムは 不利な条件下で 車両や歩行者を 確実に識別するのに苦労しています

研究 の 目的:

  • 複雑な交通環境のための強固な視覚検出フレームワークを開発する.
  • 画像の劣化にもかかわらず,車や歩行者のためのオブジェクト検出の精度を高める.

主な方法:

  • ConvIR,CIDNet,そして新しい水平/垂直強度カラースペース戦略を用いた多段階の画像強化.
  • 軽量検知アーキテクチャ,RT-DETR解読付きのMamba駆動軽量検知ネットワークで,VSSBlock,XSSBlock,VisionClueMergeモジュールを組み込む.

主要な成果:

  • 提案された方法は,交通監視データセット (0. 759 から 0. 769) で,YOLOv12sよりもmAP@50-90を1. 0パーセントポイント増加させました.
  • パラメータの複雑性と計算上のオーバーヘッドを削減した優れた展開適応性と堅牢性を実証した.

結論:

  • このフレームワークは,困難な交通条件下でのオブジェクト検出に有効なソリューションを提供します.
  • 先進的な画像強化と軽量な検出アーキテクチャの統合により,システムの性能と効率が向上します.