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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Updated: Jul 3, 2025

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在浅水中使用卷积神经网络与特征匹配进行源深度估计.

Mingda Liu1,2, Haiqiang Niu1,2, Zhenglin Li3,4

  • 1State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.

The Journal of the Acoustical Society of America
|February 11, 2024
PubMed
概括

一种新的卷积神经网络方法 (FM-CNN) 准确估计了水下源的深度. 这种先进的技术在复杂的环境中比传统的匹配场处理 (MFP) 更强大.

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科学领域:

  • 水下声学 水下声学
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 准确的水下来源定位对于海军应用至关重要.
  • 传统的匹配场处理 (MFP) 方法与环境变化作斗争.

研究的目的:

  • 提出一种基于卷积神经网络 (FM-CNN) 的新型特征匹配方法,用于水下源深度估计.
  • 与传统的MFP相比,评估FM-CNN的性能和稳定性.

主要方法:

  • 开发了一种基于卷积神经网络 (FM-CNN) 的特征匹配方法.
  • 从传播模型中使用声场复制品训练FM-CNN.
  • 将FM-CNN与传统MFP在范围独立和轻度范围依赖的环境中进行比较.
  • 对环境不匹配进行了敏感性分析 (底部参数,声速概况,地形).

主要成果:

  • 对于单个和多个源深度估计,FM-CNN对环境不匹配的稳定性比传统的MFP更高.
  • 使用来自东中国海实验的真实世界数据验证了FM-CNN的验证.
  • 在复杂的环境中,FM-CNN可靠地估计了MFP显示显著失效率的源深.

结论:

  • 拟议的FM-CNN是一种强大可靠的方法,用于在具有挑战性的声学环境中估计水下源的深度.
  • 与传统的MFP相比,FM-CNN提供了显著的优势,特别是在环境变化和复杂条件的情况下.