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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

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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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UWV-Yolox:用于水下视频物体检测的深度学习模型

Haixia Pan1, Jiahua Lan1, Hongqiang Wang1

  • 1School of Software, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

本研究介绍了UWV-Yolox,这是一种用于水下视频的增强物体检测模型. 它通过结合背景和注意力机制来提高模糊,低对比度的镜头的精度.

关键词:
协调注意力,协调注意力.框架级优化框架级优化功能损失的功能损失的功能.对象检测检测对象检测对象检测在水下拍摄视频.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 水下视频物体检测由于视频质量差 (模糊,对比度低) 而面临挑战.
  • 现有的Yolo模型与这些条件作斗争,缺乏框架对框架的上下文分析.

研究的目的:

  • 开发一个改进的视频物体检测模型,UWV-Yolox,用于具有挑战性的水下环境.
  • 为了提高低质量的水下视频中对象检测的准确性和稳定性.

主要方法:

  • 利用对比度有限的自适应式直方体平衡用于视频增强.
  • 引入了一个具有坐标注意力的CSP_CA模块,以改善对象表示.
  • 开发了一种新的损失函数 (回归和丧).
  • 实现了一个框架级优化模块,利用框架间的关系.

主要成果:

  • 在UVODD数据集上,UWV-Yolox模型实现了89.0%的mAP@0.5.
  • 与原来的Yolox模型相比,显示出3.2%的改进.
  • 与其他模型相比,展现出更稳定的对象预测.

结论:

  • UWV-Yolox有效地解决了现有的水下物体检测模型的局限性.
  • 提出的改进,包括注意力机制和框架级优化,显著提高了性能.
  • 该模型的改进可以适应其他对象检测架构.