Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: Jan 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

在移动道扫描系统中使用无参考图像质量评估优化基于深度学习的破解检测.

Chulhee Lee¹, Donggyou Kim¹, Dongku Kim¹

¹Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang-Si 10223, Gyeonggi-Do, Republic of Korea.

Sensors (Basel, Switzerland)

|September 13, 2025

概括

移动道扫描系统中的运动模糊显著降低了深度学习裂检测. 本研究引入了使用无参考图像质量评估的质量保证框架,以提高基础设施维护的模型可靠性.

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

Quality Assessment of High-Speed Motion Blur Images for Mobile Automated Tunnel Inspection.

Sensors (Basel, Switzerland)·2025

Same author

Unidirectional Real-Time Photoswitching of Diarylethene Molecular Monolayer Junctions with Multilayer Graphene Electrodes.

ACS applied materials & interfaces·2019

Same author

High-Yield Functional Molecular Electronic Devices.

ACS nano·2017

Same author

Correction to Three-Terminal Single-Molecule Junctions Formed by Mechanically Controllable Break Junctions with Side Gating.

Nano letters·2017

Same author

Gate-dependent asymmetric transport characteristics in pentacene barristors with graphene electrodes.

Nanotechnology·2016

Same author

Electrical characterization of benzenedithiolate molecular electronic devices with graphene electrodes on rigid and flexible substrates.

Nanotechnology·2016

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:

移动道扫描系统 (MTSS) 提供高效的检查.
高速度的运动模糊 (MB) 损害了深度学习裂检测的准确性.
可靠的裂检测对于智能基础设施维护至关重要.

研究的目的:

研究横向MB对MTSS图像中基于CNN的裂纹检测的影响.
提出以数据为中心的质量保证框架,使用无参考图像质量评估 (NR-IQA) 来优化模型性能.
确定有效的NR-IQA指标来评估MTSS数据中的MB.

主要方法:

故意将MB应用于公共和现实世界的MTSS数据集.
分析了ResNet,VGG和AlexNet模型中的性能变化.
将四个NR-IQA指标 (BRISQUE,NIQE,PIQE,CPBD) 与裂纹检测F1分数相关联.
开发了一个使用PIQE和CPBD值进行图像质量过的框架.

主要成果:

随着MB强度的增加,ResNet34 F1得分从89.43%降至4.45% .
PIQE和CPBD与F1分数 (分别为-0.87和+0.82) 显示出强烈的相关性,表明适合水平MB.

关键词:

卷积神经网络是一种卷积神经网络.裂纹检测裂纹检测裂纹检测移动道扫描系统扫描系统运动模糊模糊模糊没有参考的图像质量评估.

更多相关视频

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

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

Published on: November 30, 2022

相关实验视频

Last Updated: Jan 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

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

Published on: November 30, 2022

使用PIQE ≤20和CPBD ≥0.8过低质量的图像,AlexNet F1得分提高了1.46%.

结论:

水平的MB显著降低了MTSS中基于CNN的裂纹检测.
PIQE和CPBD是有效的NR-IQA指标来评估横向的MB.
拟议的以数据为中心的框架提高了MTSS数据质量,并优化了深度学习模型,以实现可靠的基础设施维护.