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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
326
Brick Classifications01:16

Brick Classifications

119
Bricks, a fundamental component of construction, are categorized based on their application and structural characteristics into several types. These include facing bricks, building bricks, hollow bricks, paving bricks, and firebricks. Facing bricks, also referred to as face bricks, are primarily used for both structural support and visual appeal, making their appearance a crucial aspect. In contrast, building bricks are typically used in concealed sections of a structure, such as behind the...
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相关实验视频

Updated: Jul 5, 2025

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
09:00

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Published on: December 19, 2016

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移动机器人的室内表面分类

Asiye Demirtaş1,2, Gökhan Erdemir3, Haluk Bayram2

  • 1Department of Electrical and Electronics Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkiye.

PeerJ. Computer science
|January 23, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种轻量级的深度学习模型,用于移动机器人表面识别,达到99.52%的准确性. 这种高效的模型非常适合具有有限计算能力的机器人,可以实现更安全的导航.

关键词:
卷积神经网络,美国有线电视新闻网.室内表面的分类室内表面的分类移动机器人 移动机器人移动网络V2 移动网络V2

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 表面类型识别对于移动机器人导航和安全至关重要.
  • 基于视觉的表面分类面临着由于外观变化 (例如地毯) 的挑战.

研究的目的:

  • 开发一个准确有效的深度学习模型用于室内表面分类 (地毯,,木材).
  • 为了创建一个适合资源有限的机器人系统的轻量级模型.

主要方法:

  • 创建了一个由2081张室内表面图像组成的新数据集.
  • 一些预先训练的深度学习模型 (InceptionV3,VGG16,等等) 进行了评估.
  • 一个修改后的,轻量级的MobileNetV2模型被提出并使用各种优化器进行了优化.

主要成果:

  • 拟议的轻量级模型实现了99.52%的准确性和99.66%的精度,回忆和F1分数.
  • 该模型的尺寸从42 MB减少到11 MB.
  • 在移动机器人上进行实时测试,准确率达到了99.25%.
  • 拟议的模型证明了加载和处理时间的缩短.

结论:

  • 拟议的轻量级模型为移动机器人领域的表面分类提供了卓越的性能和效率.
  • 这种模型非常适合嵌入式系统和具有有限计算能力的机器人.
  • 这项研究为推进机器人感知提供了有价值的数据集和优化模型.