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相关概念视频

Lumber Defects01:23

Lumber Defects

223
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...
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Detection of Black Holes01:10

Detection of Black Holes

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Wood Surfacing01:14

Wood Surfacing

146
Wood surfacing is a critical finishing process designed to smoothen the wood surface, enhance its dimensional accuracy, and make handling safer. This process compensates for potential shrinkage during the seasoning phase by marginally increasing the wood dimensions before surfacing. It also helps correct some distortions that may occur as the wood dries.
The equipment used in the surfacing process is a plane equipped with rotating blades. This tool efficiently smoothens the wood surface and can...
146
Reducing Line Loss01:18

Reducing Line Loss

196
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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相关实验视频

Updated: Sep 16, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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基于YOLOv11和边缘计算的工件表面缺陷检测.

Zishuo Wang1, Tao Ding1, Shuning Liang1

  • 1School of Information and Control Engineering, Jilin Institute of Chemical Technology, JiLin, China.

PloS one
|July 9, 2025
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概括
此摘要是机器生成的。

本研究介绍了YOLOv11用于工件表面缺陷检测,通过集成边缘计算来提高准确性和速度. YOLOv11模型显著提高了工业数据集的检测性能.

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科学领域:

  • 工业自动化 工业自动化
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 工业生产需要高质量的工件,需要有效地检测表面缺陷.
  • 目前基于云的缺陷检测方法面临着大量数据传输和降低速度的挑战.
  • 边缘计算提供了一种解决方案,可以克服计算负担并改善实时检测.

研究的目的:

  • 使用YOLOv11和边缘计算提出一种高效和精确的工件表面缺陷检测方法.
  • 通过数据增强和生成对抗网络来提高YOLOv11模型的性能.
  • 验证模型的通用性,并将其部署在边缘设备上,以提高检测速度.

主要方法:

  • 使用随机翻转,裁剪和自我注意力生成对抗网络 (SA-GAN) 的数据集扩展.
  • 在NEU-DET和Tianchi形状表面缺陷数据集上对YOLOv7与YOLOv11模型进行比较分析.
  • 将基于云的YOLOv11模型转换为基于边缘的YOLOv11-RKNN模型,用于部署在RK3568边缘设备上.

主要成果:

  • 在NEU-DET数据集上,与SA-GAN配合的YOLOv11表现出优异的mAP@0.5改进,相比于YOLOv7,YOLOv8,YOLOv9和YOLOv10.
  • 该模型在Tianchi数据集上获得了87.0%的mAP@0.5,证实了其通用性.
  • 边缘部署减少了59.3%的模型大小和35.5%的单图像检测时间 (从52.1ms降至33.6ms).

结论:

  • 提议的YOLOv11模型与SA-GAN增强,为工件表面缺陷检测提供了显著的精度改进.
  • 边缘部署YOLOv11为工业应用提供了显著的检测速度和效率的增强.
  • 这些发现证实了YOLOv11在现实世界工业缺陷检测任务中的有效性和通用性.