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

Introduction to Learning01:18

Introduction to Learning

476
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
476

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

RoadDiffBox: Automatic Road Distress Diagnosis through Controlled Image Generation and Semi-Supervised Learning.

Research (Washington, D.C.)·2025
Same author

Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement.

Sensors (Basel, Switzerland)·2025
Same author

Lightweight deep learning for real-time road distress detection on mobile devices.

Nature communications·2025
Same author

Introduction to 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2023
Same author

A Longitudinal Functional Magnetic Resonance Imaging Study of Working Memory in Patients Following a Transient Ischemic Attack: A Preliminary Study.

Neuroscience bulletin·2018
Same author

Micro-nanostructured δ-Bi<sub>2</sub>O<sub>3</sub> with surface oxygen vacancies as superior adsorbents for SeO<sub>x</sub><sup>2-</sup> ions.

Journal of hazardous materials·2018

相关实验视频

Updated: Jul 23, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

自动识别路面纹理,使用轻量级的几次射击学习.

Shuo Pan1, Hai Yan1, Zhuo Liu1

  • 1Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, People's Republic of China.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|July 16, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于路面纹理识别的几次拍摄学习模型,克服了数据稀缺性. 语网络模型实现了高精度,为道路维护专业人员提供了高效的解决方案.

关键词:
西安人的网络网络.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.一个维的卷积.路面检测 路面检测

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

9.0K
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

9.5K

相关实验视频

Last Updated: Jul 23, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
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

9.0K
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

9.5K

科学领域:

  • 土木工程 土木工程是指土木工程.
  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学

背景情况:

  • 路面结构显著影响道路性能和安全.
  • 准确的路面纹理识别对于有效的道路维护和危险检测至关重要.
  • 在路面纹理分析中,有限的数据集对传统的深度学习模型构成了挑战.

研究的目的:

  • 使用有限的数据开发一款用于路面纹理识别的几次拍摄学习模型.
  • 为了解决路面纹理分类中的数据稀缺问题.
  • 为了创建适合工程实践的轻量级模型.

主要方法:

  • 提出了一个基于语网络的几次学习模型.
  • 实施全球平均聚合 (GAP) 和一维卷积以优化模型.
  • 进行比较实验,以评估模型性能,存储和训练时间.

主要成果:

  • 在四向五拍路面纹理分类任务中获得了89.8%的准确性.
  • 轻量级模型显著减少了存储容量 (高达94%) 和训练时间 (高达99%).
  • 使用GAP的模型实现了最高的准确性 (93.5%),同时减少了83%的存储和6%的培训时间.

结论:

  • 短暂的学习有效地解决了路面纹理识别中的数据稀缺问题.
  • 优化的轻量级模型为现实世界的工程应用提供了实际的解决方案.
  • 拟议的基于罗网络的方法提高了路面安全性和维护效率.