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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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相关实验视频

Updated: Jul 21, 2025

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

572

使用最佳复合骨干网络进行新型的水下图像增强.

Yuhan Chen1, Qingfeng Li2, Dongxin Lu2

  • 1Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

Biomimetics (Basel, Switzerland)
|July 28, 2023
PubMed
概括

研究人员开发了一种最佳的水下图像增强复合骨干网络 (OECBNet),以提高图像质量和速度. 这种新方法超越了现有的基于卷积神经网络 (CNN) 的水下图像增强技术.

关键词:
复合架构是复合的架构.复合骨干的复合骨干是一种复合骨干.深度学习是一种深度学习.水下图像增强水下图像增强

更多相关视频

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Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
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Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

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科学领域:

  • 海洋技术 海洋技术
  • 计算机视觉 计算机视觉 计算机视觉
  • 图像处理 图像处理

背景情况:

  • 水下图像处理对于海洋探索至关重要.
  • 现有的基于卷积神经网络 (CNN) 的方法具有有限的特征学习能力.
  • 当前的方法往往会在增强质量和实时性能之间妥协.

研究的目的:

  • 开发一个最佳的水下图像增强复合骨干网络 (OECBNet).
  • 提高水下图像增强效果,减少处理时间.
  • 解决现有CNN的功能学习和网络复杂性的局限性.

主要方法:

  • 对不同的复合骨干架构进行了全面的研究.
  • 评估的是骨干数量,连接策略,修剪策略和辅助损失.
  • 一个优化的复合骨干网络 (CBNet) 被确定并完善.

主要成果:

  • 拟议的OECBNet与现有的基于CNN的方法相比,展示了优越的图像增强.
  • 实验证实了OECBNet在改善水下图像质量的有效性.
  • 优化的网络在运行时间缩短的情况下取得了更好的结果.

结论:

  • OECBNet在水下图像增强方面取得了重大进展.
  • 这种优化的复合骨干网络有效地平衡了增强质量和实时性能.
  • OECBNet为实时水下图像处理挑战提供了一个有前途的解决方案.