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相关概念视频

Lumber Defects01:23

Lumber Defects

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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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Steel Manufacturing01:26

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Steel manufacturing is a multi-stage process that begins by smelting iron ore into cast iron in a blast furnace. This initial stage involves layering iron ore with coke, a type of fuel, and crushed limestone within the furnace. The coke is ignited with a high volume of air, leading to the creation of carbon monoxide, which acts to reduce the iron ore to pure iron.
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Mechanical Characteristics of Steel01:18

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The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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钢管的表面缺陷检测方法基于改进的YOLO.

Lili Wang1,2,3,4, Chunhe Song1,2,3, Guangxi Wan1,2,3

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.

Mathematical biosciences and engineering : MBE
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PubMed
概括

本研究介绍了一种基于YOLO的改进方法,用于检测钢管表面缺陷. 这种新的方法通过解决类似的缺陷外观和不同大小等挑战来提高准确性,从而提高质量控制.

关键词:
图像X射线图像X射线图像X射线图像X射线图像这是YOLOv5的.深度学习是一种深度学习.发现缺陷检测检测缺陷检测钢管是钢管中的一个.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机视觉 计算机视觉
  • 质量控制 质量控制 质量控制

背景情况:

  • 钢管质量保证在很大程度上依赖于表面缺陷检测.
  • 挑战包括类似的缺陷外观,尺度变化和小目标检测.

研究的目的:

  • 开发一种先进的钢管表面缺陷检测方法.
  • 为了提高检测准确性和解决现有算法的局限性.

主要方法:

  • 一个新的骨干块增强了类似缺陷的特征提取.
  • 一个新的子阻塞通过融合多尺度特征来改善小缺陷的检测.
  • 一个定制的回归损失函数和焦点损失解决尺度变化和数据不平衡.

主要成果:

  • 拟议的方法显著提高了钢管表面缺陷检测的准确性.
  • 增强的特征提取和多尺度融合有助于更高的检测率.
  • 新的损失函数有效地处理缺陷大小和样本不平衡的变化.

结论:

  • 开发的基于YOLO的框架为钢管表面缺陷检测提供了强大的解决方案.
  • 该方法有效地克服了与特征相似性,规模变化和小目标相关的挑战.
  • 这一进步有助于提高钢管制造业的质量控制.