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相关概念视频

Deformation in a Circular Shaft01:10

Deformation in a Circular Shaft

389
One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
389
Plastic Deformation in Circular Shafts01:20

Plastic Deformation in Circular Shafts

209
When materials are subjected to forces that surpass their yield strength, they undergo a process known as plastic deformation. This results in a permanent alteration or strain in their structure. This concept can be specifically applied to circular shafts, where the deformation leads to a change in its shape. The precise evaluation of this plastic deformation requires understanding the stress distribution within the circular shaft, which is achieved by calculating the maximum shearing stress in...
209
Transmission Shafts: Problem Solving01:09

Transmission Shafts: Problem Solving

266
Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
Next, use bending moment diagrams for the shaft to...
266
Design of Transmission Shafts - Stress Analysis01:15

Design of Transmission Shafts - Stress Analysis

405
Designing a transmission shaft requires a thorough understanding of the stresses induced by bending moments and torques, especially in systems where power is transferred through gears. These forces create force-couple systems at the centers of the shaft's cross-sections, leading to both transverse and torsional loading. Although shearing stresses from transverse loads are typically smaller than those from torques and are often overlooked, the significant normal stresses from these loads...
405
Residual Stresses in Circular Shafts01:10

Residual Stresses in Circular Shafts

200
In materials that exhibit elastic and plastic behavior, known as elastoplastic materials, residual stresses can accumulate when these materials experience plastic deformation. This deformation arises from either high levels of shearing stress or significant strains. Residual stresses are internal stresses that persist within a material after removing the external force causing deformation. This phenomenon is demonstrated when observing the behavior of a shaft under torque; notably, the...
200
Lumber Defects01:23

Lumber Defects

153
Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
153

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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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STMS-YOLOv5:用于轮表面缺陷检测的轻量级算法

Rui Yan1,2, Rangyong Zhang1,2, Jinqiang Bai1,2

  • 1Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.

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|July 14, 2023
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概括
此摘要是机器生成的。

一个新的轻量级深度学习模型,STMS-YOLOv5,显著提高了轮表面缺陷检测的速度和准确性. 这种模型可以降低计算成本,同时保持工业应用的高性能.

关键词:
注意力机制注意力机制轮缺陷检测 轮缺陷检测 轮缺陷检测轻量级网络轻量级的网络.

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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
  • 材料科学 材料科学 材料科学

背景情况:

  • 深度学习对象检测模型在轮表面缺陷检测方面面临挑战,原因是高计算需求和复杂的架构,导致速度和准确性低于最佳.
  • 现有的方法在实时工业检查场景中难以平衡效率和性能.

研究的目的:

  • 提出一种新的轻量级深度学习模型STMS-YOLOv5,用于高效准确地检测轮表面缺陷.
  • 解决当前算法在速度,准确性和计算成本方面的局限性.

主要方法:

  • 使用ShuffleNetv2模块实现轻量级的骨干,以最大限度地减少GFLOP和参数.
  • 综合转换卷积上采样,以增强网络学习能力.
  • 嵌入了最大效率的通道注意力机制,以抵消来自轻量级骨干的准确性损失.
  • 使用SIOU_Loss用于界限框回归以加快模型收.

主要成果:

  • 实现了130.4 FPS (轮数据集) 和133.5 FPS (NEU-DET钢铁数据集) 的高推断速度.
  • 与基线模型相比,模型参数减少了44.4%,GFLOPs减少了50.31%.
  • 在轮数据集上获得高平均精度 (mAP@0.5),在轮数据集上达到98.6%,在NEU-DET数据集上达到73.5%.

结论:

  • 拟议的STMS-YOLOv5模型为工业表面缺陷检测提供了轻量级深度学习的重大进步.
  • 在速度,参数减少和准确性方面表现出卓越的性能,验证了其有效性和概括能力.