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基于TinyML的小型机器人的管道内特征检测.

Manman Yang1, Andrew Blight1, Hitesh Bhardwaj1

  • 1School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK.

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|April 28, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种利用微型机器人微型机器学习 (TinyML) 的资源高效的管道特征识别方法. TinyML模型准确地识别管道特征,使小直径管道的自主导航成为可能.

关键词:
卷积神经网络 (CNN) 是一种神经网络.管道内特征检测检测 管道内特征检测迷你机器人迷你机器人资源效率高,使用资源高效.微小的机器学习 (TinyML)

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 在直径小的管道中自主导航对于微型机器人至关重要.
  • 微型机器人有限的计算资源给环境感知带来了挑战.

研究的目的:

  • 为微型机器人使用微型机器学习 (TinyML) 提出一种资源高效的管道特征识别方法.
  • 为了使微型机器人能够识别自主导航的关键管道特征.

主要方法:

  • 开发了一个定制的五层卷积神经网络 (CNN),为资源有限的设备进行了优化.
  • 在 4629 个不同的管道图像的自定义数据集上训练了 CNN 模型.
  • 实施了移动窗口平滑策略,以在具有挑战性的条件下保持稳定的性能.

主要成果:

  • 在识别管道特征方面达到97.1%的高精度.
  • 证明了低资源利用:195.1kB的峰值RAM,427.9kB的闪存使用.
  • 推理时间为1693毫秒,表明计算效率.

结论:

  • TinyML模型可以有效地部署在资源有限的微型机器人上,用于管道特征识别.
  • 拟议的方法为自主管道内勘探和检查提供了具有成本效益的解决方案.
  • 先进的机器学习是可行的提高机器人自主性在具有挑战性的环境.