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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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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 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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一个基于深度信息的适应性多尺度网络,用于群众计数.

Peng Zhang1, Weimin Lei1, Xinlei Zhao2

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个适应性的多尺度网络,用于人群计数,提高计算机视觉任务的准确性和速度,如监视. 这种新的方法有效地处理不同的人群密度和遮,优于现有的方法.

关键词:
在美国,CNN是CNN.人群计数的人群计数深度学习是一种深度学习.计数对象 计数对象

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 群众计数对于公共安全和智能运输至关重要,但面临着诸如阻塞,不一致的目标大小和注释错误等挑战.
  • 现有的密度图谱回归方法难以处理尺度变化和区分近距离目标,导致稀疏区域的性能差.

研究的目的:

  • 为准确和高效的人群计数提出一个可适应的多尺度网络.
  • 解决现有方法在规模适应性和近距离目标之间的特征区分方面的局限性.

主要方法:

  • 使用卷积神经网络 (CNN) 框架开发了一个可适应的多尺度远近距离网络.
  • 采用堆叠的卷积层用于更深的网络,根据目标距离分配不同的受体场,并融合了附近的特征.
  • 集成的深度信息和像素级的自适应建模,通过将图像划分为补丁.
  • 使用密度规范平均精度 (nAP) 用于空间定位精度分析.

主要成果:

  • 拟议的网络在准确性,推断速度和性能之间取得了良好的平衡.
  • 在具有挑战性的基准指标 (上海科技A/B,UCF_CC_50,UCF-QNRF) 上,与最先进的 (SOTA) 方法相比,表现显著改善.
  • 有效地处理复杂的背景和多样化的人群分布,包括严重的遮和尺度变化.

结论:

  • 适应式多尺度网络为各种场景中的人群计数提供了强大的解决方案.
  • 该方法增强了特征提取和适应性模型种群,克服了传统方法的局限性.
  • 在多个数据集上验证了有效性,在复杂的环境因素下显示出卓越的性能和稳定性.