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相关概念视频

Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
Conservation of Mass in Moving, Nondeforming Control Volume01:14

Conservation of Mass in Moving, Nondeforming Control Volume

Stormwater detention basins are essential in managing runoff during heavy rainfall, particularly in urban areas where impervious surfaces increase the risk of flooding. Understanding the conservation of mass in these systems allows engineers to optimize basin performance, balancing inflow, outflow, and water storage.
In the context of a detention basin, the conservation of mass states that the total mass of water entering the basin must equal the mass leaving the basin plus any accumulation of...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
Gradually Varying Flow01:29

Gradually Varying Flow

Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

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相关实验视频

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Deep Fluorescence Observation in Rice Shoots via Clearing Technology
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GCPDFFNet:用于识别大米爆破的小型物体检测

Dejin Xie1,2, Wei Ye1,2, Yining Pan1

  • 1College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.

Phytopathology
|July 5, 2024
PubMed
概括
此摘要是机器生成的。

早期发现大米爆发病对作物产量至关重要. 一个新的模型,GCPDFFNet,使用先进的特征融合和新型损失函数准确识别小米爆破病变,提高检测准确度和速度.

关键词:
卷积神经网络是一种卷积神经网络.识别大米炸弹的识别方式小物体检测 小物体检测

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科学领域:

  • 农业科学 农业科学
  • 植物病理学 植物病理学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 爆病对全球大米生产和粮食安全构成重大威胁.
  • 准确及时检测大米爆发对于有效的疾病管理和产量保存至关重要.
  • 现有的检测方法可能会与大米爆破病变的小尺寸和多样化外观作斗争.

研究的目的:

  • 开发和评估一种新的深度学习模型,用于在现场早期和准确地检测爆病.
  • 为了应对在现场图像中识别小,不规则形状的米爆病变的挑战.
  • 提高自动化大米爆炸检测系统的效率和准确性.

主要方法:

  • 构建一个专门的米爆炸数据集,具有多样化的损伤特征 (形状,大小,颜色).
  • 基于全球背景的并行差异化特征融合网络 (GCPDFFNet) 的提议,包括全球背景和并行差异化特征融合模块.
  • 引入SCYLLA规范化的瓦斯斯坦距离损失函数,以增强模型融合和检测准确性.

主要成果:

  • 与基线CenterNet.Net相比,GCPDFFNet模型在平均平均精度 (mAP) 中取得了显著改善,从83.6%增加到95.4%.
  • 该模型保持了每秒122.1的高推断速度,满足实时检测要求.
  • 实验验证证证实了GCPDFFNet在准确识别大米爆破病变方面的卓越性能.

结论:

  • 拟议的GCPDFFNet模型证明了在现场检测爆病的高准确性和效率.
  • 新的网络架构和损失功能有效地解决了小损伤识别的挑战.
  • 这一进步有可能在精准农业和作物疾病管理系统中得到实际应用.