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相关概念视频

Minor Losses in Pipes01:25

Minor Losses in Pipes

613
In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
Valves play a significant role in generating minor losses by obstructing or redirecting the fluid flow. When a valve is closed or partially closed, it restricts the flow...
613
Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

278
A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is...
278
Multiple Pipe Systems01:21

Multiple Pipe Systems

409
Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
409
Pipe Flowrate Measurement01:28

Pipe Flowrate Measurement

285
In pipe flow measurement, orifice, nozzle, and Venturi meters are commonly used to determine fluid flowrates by constricting the flow area, which increases fluid velocity and reduces pressure. This pressure difference, governed by Bernoulli's principle and adjusted for real-world conditions, is essential for calculating flowrate. Each meter type is suited to specific applications based on accuracy, efficiency, and compatibility with various flow conditions.
The orifice meter is a simple,...
285
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

81
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
81
Single Pipe Systems01:24

Single Pipe Systems

102
In pipe flow analysis, problems are typically categorized into three types — Type I, Type II, and Type III — based on the known parameters and the desired outcome. Each type of problem addresses specific engineering requirements using fluid properties, pipe characteristics, and operational conditions.
In a Type I problem, fluid properties (density and viscosity), pipe characteristics (including diameter, length, and surface roughness), and the flow rate or average velocity are...
102

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相关实验视频

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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FP-YOLOv8:基于改进的YOLOv8n车管末端的表面缺陷检测算法

Ke Rao1, Fengxia Zhao1, Tianyu Shi1

  • 1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
概括

一个新的轻量级算法FP-YOLOv8增强了车管末端的表面缺陷检测. 它提高了准确性,并减少了模型大小,使其成为在线检查系统的理想选择.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 材料科学 材料科学 材料科学

背景情况:

  • 现有的深度学习算法难以检测制动管末端的表面缺陷.
  • 需要轻量级和准确的模型来实时检测缺陷.

研究的目的:

  • 提出一种新的轻量级深度学习算法FP-YOLOv8,用于改进车管末端的表面缺陷检测.
  • 为了提高检测准确度和减少在线应用程序的模型复杂性.

主要方法:

  • 基于YOLOv8n框架开发了FP-YOLOv8,结合了C2f_GhostV2模块和脱完全连接 (DFC) 注意力机制.
  • 实施了动态ATSS标签分配策略和使用深度可分离卷积的不对称小目标检测头 (FADH).

主要成果:

  • FP-YOLOv8实现了89.5%的mAP50和87%的F1得分,其表现分别超过了YOLOv8n的3.3%和6.0%.
  • 该模型将参数降低了14.3%,计算成本降低了21.0%,AP50对裂 (5.5%),划痕 (5.6%) 和闪光灯缺陷 (2.3%) 的显著改进.

结论:

  • FP-YOLOv8有效地提高了缺陷检测的准确性,并减少了车管末端表面的错过检测.
关键词:
这就是YOLOv8n.车管的末端结束了车管的结束.标签的分配 标签的分配表面缺陷检测检测表面缺陷检测

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  • 该算法的轻量级设计和改进的性能满足了在线缺陷检测系统的需求.