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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.3K
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...
6.3K
Flame Photometry: Overview01:02

Flame Photometry: Overview

547
Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
547
Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Flame Photometry: Lab01:16

Flame Photometry: Lab

232
In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
232
Detection of Black Holes01:10

Detection of Black Holes

2.2K
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...
2.2K
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K

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

PIAS1-mediated GSK3β SUMOylation exacerbates tauopathy and cognitive deficits in Alzheimer's disease models.

Molecular psychiatry·2026
Same author

Embedded Three-Dimensional Bubble-Like Polar Textures with Ionic-Ordering-Assisted Stabilization in CuInP<sub>2</sub>S<sub>6</sub>.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

A high-resolution gridded dataset of biomass resource potentials in China with policy-aware land-use constraints for renewable energy planning.

Scientific data·2026
Same author

Predictive model of sarcopenia in chronic kidney disease: an integrated approach of bioinformatics, machine learning, and clinical validation.

Frontiers in physiology·2026
Same author

Cardiac monitoring in adolescent and young adult cancer patients treated with anthracyclines: a longitudinal descriptive study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Galectin-3 in microglia mediates neuroinflammation-induced cognitive dysfunction via selective elimination of excitatory synapses in hippocampal CA1.

Brain research·2026

相关实验视频

Updated: Jun 23, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

基于YOLOv5s的小型目标的轻量级火灾检测算法.

Changzhi Lv1, Haiyong Zhou2, Yu Chen1

  • 1National Experimental Teaching Demonstration Center for Electrical Engineering and Electronics, College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.

Scientific reports
|June 18, 2024
PubMed
概括

这项研究引入了改进的YOLOv5s火灾检测算法,提高了复杂环境中的准确性和小目标识别. 这种轻量级模型实现了高精度和实时检测速度.

更多相关视频

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K
Additive Manufacturing-Enabled Low-Cost Particle Detector
06:05

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

1.2K

相关实验视频

Last Updated: Jun 23, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K
Additive Manufacturing-Enabled Low-Cost Particle Detector
06:05

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

1.2K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 消防安全工程 消防安全工程

背景情况:

  • 目前的火灾检测算法在复杂的环境中难以准确识别小目标.
  • 现有的方法往往缺乏实时应用所需的效率.

研究的目的:

  • 开发一种轻量级且准确的火灾检测算法.
  • 改进在具有挑战性的环境中识别小型火灾目标.
  • 为了满足对增强消防安全的实时检测需求.

主要方法:

  • 一个改进的YOLOv5s架构,包含了上下文变压器 (CoT) 和一个新的CSP1_CoT模块.
  • 对Neck架构的改进,包括一个专门的小目标检测层和SE注意力机制.
  • 实施焦效IOU (Focal-EIoU) 损失函数,以提高收度和精度.

主要成果:

  • 修改后的模型实现了96%的平均平均精度 (mAP@.5) 和94.8%的精度,分别提高了8.8%和8.9%.
  • 减少了1.1%的模型参数数量,以一个紧的14.6MB大小.
  • 实现了每秒85 (FPS) 的检测速度,满足实时要求.

结论:

  • 增强的YOLOv5s算法为火灾检测提供了卓越的精度和准确性.
  • 轻量级设计和高检测速度满足实时和资源有限的应用需求.
  • 这种改进的算法有效地解决了当前火灾检测系统的局限性.