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相关概念视频

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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相关实验视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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使用卷积神经网络对多个可见光通信细胞进行移动识别和定位.

Xiaoxiao Du, Yanyu Zhang, Chao Wang

    Optics letters
    |December 15, 2023
    PubMed
    概括

    本研究引入了工业物联网 (IIoT) 的新框架,使用LED图像功能来准确识别和定位移动机器人和车辆. 该系统实现了高精度,增强了可见光通信服务.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器人技术 机器人技术 机器人技术
    • 事物的工业互联网 (IIoT)

    背景情况:

    • 工业环境需要强大的通信和精确的定位,用于移动生产项目,如机器人和自动引导车辆 (AGV).
    • 现有的感知系统往往难以将通信与移动识别和定位的同时整合起来.
    • 精确的多对象识别和空间意识对于先进的IIoT应用至关重要.

    研究的目的:

    • 为IIoT环境提出一个新的多光学细胞识别和定位框架.
    • 为了利用LED图像功能来增强感知和定位能力.
    • 在工业环境中提供可见光通信的位置感知服务.

    主要方法:

    • 使用CMOS图像传感器从生产项目中捕获LED图像.
    • 采用卷积神经网络 (CNN) 来训练和识别基于LED图像特征的多个光细胞.
    • 在光学单元中实现区域识别,用于精确定位.

    主要成果:

    • 美国有线电视新闻网 (CNN) 模型在识别两个LED电池时,达到99%以上的平均准确率.
    • 光学单元内的区域识别显示了高达100%的平均准确性.
    • 拟议的框架显著超过了传统的识别算法.

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    结论:

    • 开发的框架有效地将通信与在IIoT中的准确识别和定位相结合.
    • 高精度的LED图像特征识别和定位可以使用CNN实现.
    • 该系统为IIoT应用可见光通信中的位置感知服务提供了一个有前途的解决方案.