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

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

[Effects of elevated atmospheric CO2 on paddy soil nitrogen content during rice season].

Ying yong sheng tai xue bao = The journal of applied ecology·2010
Same author

Preliminary proposal for surgical classification of sacral tumors.

Journal of neurosurgery. Spine·2010
Same author

Evaluation of Psn, HmuR and a modified LcrV protein delivered to mice by live attenuated Salmonella as a vaccine against bubonic and pneumonic Yersinia pestis challenge.

Vaccine·2010
Same author

Microprinting of liver micro-organ for drug metabolism study.

Methods in molecular biology (Clifton, N.J.)·2010
Same author

Eicosapentaenoic acid disrupts the balance between Tregs and IL-17+ T cells through PPARγ nuclear receptor activation and protects cardiac allografts.

The Journal of surgical research·2010
Same author

[Progress in the study of heat shock protein 90 inhibitors].

Yao xue xue bao = Acta pharmaceutica Sinica·2010

相关实验视频

Updated: May 10, 2025

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
12:45

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Published on: August 31, 2022

2.7K

使用SPGD和生成对抗网络融合的道裂分割和识别算法.

Wei Sun1,2, Xiaohu Liu3, Zhiyong Lei1,4

  • 1School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, China.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
概括

这项研究引入了针对无人机的新道裂分割算法,将静态平行梯度下降 (SPGD) 算法与生成对抗网络 (GAN) 融合在一起. 这种方法显著提高了裂识别率,特别是对于小裂.

关键词:
生产对抗性网络 (GAN) 的产生.图像分割 图像细分 图像细分随机的平行梯度下降 (SPGD)一个道裂,一个道裂.

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

319
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

相关实验视频

Last Updated: May 10, 2025

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
12:45

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Published on: August 31, 2022

2.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

319
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 土木工程 土木工程是指土木工程.

背景情况:

  • 探测道裂对于基础设施安全至关重要.
  • 无人机上现有的基于视觉的系统在准确识别小或有纹理的裂方面面临挑战.
  • 需要自动和精确的裂细分来提高检查效率.

研究的目的:

  • 为无人机视觉系统开发先进的道裂细分算法.
  • 为了提高识别精度和道裂的细节提取.
  • 为了提高小型和纹理道裂的检测率.

主要方法:

  • 一个新的算法,将静态并行梯度下降 (SPGD) 算法与生成对抗网络 (GAN) 融合在一起.
  • SPGD算法用于增强图像细节和边缘信息.
  • 一个包含改进的U-Net生成器和用于细分的全卷积网络 (FCN) 区分器的GAN.

主要成果:

  • 拟议的算法显著提高了道裂图像的清晰度和细节.
  • 实现了道裂的有效细分,特别是具有复杂纹理的小型裂.
  • 对12个典型的道裂纹图像的实验验证表明,与其他方法相比,识别率大幅增加.

结论:

  • SPGD和GAN融合算法为基于无人机的道裂识别提供了一种优越的方法.
  • 增强的细节和细分功能解决了检测小型和纹理裂的局限性.
  • 这种方法有望提高道基础设施的安全性和维护性.