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

Force Classification01:22

Force Classification

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,...

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

Tensor Multi-Subspace Representation for Remote Sensing Image Mixed Noise Removal.

IEEE transactions on neural networks and learning systems·2025
Same author

Multimodal Quaternion Representation Network for Multisource Remote Sensing Data Classification.

IEEE transactions on neural networks and learning systems·2025
Same author

Tensor network decomposition for data recovery: Recent advancements and future prospects.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Fully Tensorized Lightweight ConvLSTM Neural Networks for Hyperspectral Image Classification.

IEEE transactions on neural networks and learning systems·2025
Same author

Global Clue-Guided Cross-Memory Quaternion Transformer Network for Multisource Remote Sensing Data Classification.

IEEE transactions on neural networks and learning systems·2024
Same author

An Efficient Supervised Deep Hashing Method for Image Retrieval.

Entropy (Basel, Switzerland)·2023

相关实验视频

Updated: Jul 16, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K

FBD-SV-2024:在监视视频中检测飞鸟物体的数据集

Zi-Wei Sun1, Ze-Xi Hua2, Heng-Chao Li3

  • 1Southwest Jiaotong University, School of Information Science and Technology, Chengdu, 611756, China.

Scientific data
|March 29, 2025
PubMed
概括

一个新的数据集,监控视频飞鸟数据集 (FBD-SV-2024),有助于开发和评估飞鸟检测算法. 目前的先进方法仍然发现这个数据集具有挑战性.

更多相关视频

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

8.9K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.1K

相关实验视频

Last Updated: Jul 16, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K
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

8.9K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.1K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 野生动物监测 野生动物监测

背景情况:

  • 在监控录像中准确检测飞行鸟类对于各种应用至关重要,包括野生动物管理和公共安全.
  • 现有的数据集可能无法充分反映现实世界监控场景的复杂性,例如小物体大小和多样化的外观.

研究的目的:

  • 引入监控视频的飞鸟数据集 (FBD-SV-2024),这是一个用于训练和比较飞鸟检测算法的新型资源.
  • 提供具有挑战性的基准,反映出评估算法性能的现实监控条件.

主要方法:

  • 策划了FBD-SV-2024数据集,包括483个视频片段,有28694个,包括28366个注释的飞鸟实例.
  • 该数据集在现实的监视设置中捕捉鸟类,突出了诸如不显眼的特征,小尺寸和形状变化等挑战.

主要成果:

  • 在FBD-SV-2024数据集上使用最先进的视频物体检测算法进行了实验.
  • 结果表明,即使是先进的算法也难以在这个具有挑战性的数据集上实现最佳性能,这凸显了它的难度.

结论:

  • FBD-SV-2024数据集对当前的飞鸟检测技术构成了重大挑战.
  • 这一数据集将推动在监视应用中用于鸟类检测的更强大,更准确的算法的开发.