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相关概念视频

Lumber Defects01:23

Lumber Defects

148
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...
148
Mechanical Characteristics of Steel01:18

Mechanical Characteristics of Steel

602
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.
The tension test is fundamental for determining tensile strength. In this test, a steel specimen is stretched using a gripping device until it breaks. The data collected during this test are used...
602
Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

132
The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...
132

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相关实验视频

Updated: Jul 16, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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EFC-YOLO:一个高效的表面缺陷检测算法用于钢带.

Yanshun Li1, Shuobo Xu1, Zhenfang Zhu1

  • 1School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了EFC-YOLO,这是一个高效的深度学习模型,用于检测钢表面缺陷. 它在不增加模型大小的情况下实现了高精度和速度,提高了工业检查能力.

关键词:
这就是YOLOv7的意义.深度学习是一种深度学习.功能提取 特性提取表面缺陷检测检测表面缺陷检测

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相关实验视频

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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
  • 人工智能的人工智能

背景情况:

  • 钢表面缺陷检测对于质量控制至关重要.
  • 现有的方法往往难以平衡准确性,速度和模型大小.
  • 研究旨在提高识别精度和速度,同时减少模型足迹.

研究的目的:

  • 提出一个改进的高效融合协调网络 (EFC-YOLO),用于高速和高精度的钢表面缺陷检测.
  • 为了增强特征提取和位置信息依赖性,而不会增加模型大小.
  • 优化功能融合,以获得更好的检测性能.

主要方法:

  • 在YOLOv7骨干中整合了一种改进的具有部分卷积 (PConv) 的融合加速模块.
  • 整合了快捷方式坐标注意力 (SCA) 机制,以提高位置意识.
  • 使用减重的双向特征金字塔网络 (BiFPN) 来改善特征融合.

主要成果:

  • 在NEU-DET数据集上,EFC-YOLO模型实现了85.9%的平均精度 (mAP).
  • 与基线模型相比,计算成本 (GFLOPs) 降低了60%.
  • 在模型大小,检测准确度和推断速度之间展示了有效的平衡.

结论:

  • 拟议的EFC-YOLO模型在钢表面缺陷检测方面取得了重大进展.
  • 整合PConv,SCA和减权BiFPN有助于提高效率和准确性.
  • 这种方法为实时,高精度的工业检查系统提供了可行的解决方案.