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

Survival Tree01:19

Survival Tree

374
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
374

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

A fuzzy multiple-attribute decision-making under Z-number environment for online teaching effectiveness evaluation for college database technology courses.

Scientific reports·2026
Same author

Atomistic insights into the degradation of perfluorosulfonic acid membranes: A reactive force field molecular dynamics study.

PloS one·2026
Same author

CNATNet: a convolution-attention hybrid network for safflower classification.

Frontiers in plant science·2025
Same author

Superhydrophilic S-NiFe LDH by Room Temperature Synthesis for Enhanced Alkaline Water/Seawater Oxidation at Large Current Densities.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Detection of the Content of Two Coumarins, IM and ISOIM, and Their Mechanism of Action on Colitis Rats in Angelica albicans.

Computational and mathematical methods in medicine·2022
Same author

The Neutrophil-to-Lymphocyte and Monocyte-to-Lymphocyte Ratios Are Independently Associated With the Severity of Autoimmune Encephalitis.

Frontiers in immunology·2022
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

448

基于深度学习的森林火灾检测使用改进的SSD算法与CBAM.

Diansheng Zhang1, Yueyuan Zhang1, Leilei Dong2

  • 1School of Information and Software Engineering, East China Jiaotong University, Nanchang, China.

PloS one
|November 18, 2025
PubMed
概括
此摘要是机器生成的。

一个新的CBAM-SSD模型通过增强火焰和烟雾识别来改善森林火灾检测. 这种物体检测系统为实时野火监测提供了更高的准确性和更少的错误报警.

更多相关视频

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

999
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.0K

相关实验视频

Last Updated: Jan 11, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

448
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

999
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.0K

科学领域:

  • 计算机科学 计算机科学
  • 环境科学 环境科学
  • 人工智能的人工智能

背景情况:

  • 森林火灾由于迅速蔓延和具有破坏性潜力,造成了重大生态威胁.
  • 现有的森林火灾检测系统与可变的火焰/烟雾特征和环境干扰作斗争,导致错误阳性和错误检测.

研究的目的:

  • 开发一种新的物体检测模型,CBAM-SSD,以改进森林火灾检测.
  • 在复杂的环境条件下提高检测火焰和烟雾的准确性和可靠性.

主要方法:

  • 采用数据增强 (几何,颜色转换) 来解决数据限制.
  • 将CBAM模块集成到SSD骨干中,以进行自适应性特征提取,专注于关键火灾区域.
  • 重建了特征提取结构,以改善可变火焰和烟雾特征的感知.

主要成果:

  • 对于火焰和烟雾,CBAM-SSD实现了97.55%的mAP@0.5,比基线SSD有1.53%的改善.
  • 火焰检测AP50达到96.61% (3.01%的增长),其中96.40%的回忆.
  • 烟雾检测AP50达到98.49%,回忆率为98.80%,显示出更高的精度和更少的检测错误.

结论:

  • 该CBAM-SSD模型轻量级,适合实时森林火灾检测.
  • 该模型显著提高了检测准确性,并减少了错误和错过的检测.
  • 为实时森林火灾监测提供了一种高效,方便和准确的解决方案.