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    科学领域:

    • 计算机视觉 计算机视觉
    • 传感器技术 传感器技术
    • 机器学习 机器学习

    背景情况:

    • 在雾等恶劣天气条件下,姿势分类具有挑战性.
    • 现有的方法可能会在动态和异质的雾环境中扎.
    • 低成本的传感器对于广泛的消费电子应用是可取的.

    研究的目的:

    • 开发和评估用于使用单光子雪崩二极管 (SPAD) 阵列对木制人形姿势进行分类的框架.
    • 为了评估极端学习机器 (ELM) 在雾中快速分类姿势的性能.
    • 以低成本的传感器系统来证明实时姿势分类的可行性.

    主要方法:

    • 在雾室内使用了低成本的SPAD阵列传感器与扩散VCSEL激光器.
    • 通过对传感器固件进行时间和空间分辨率的配置来收集训练数据集.
    • 训练了一台极端学习机器 (ELM) 用于姿势分类,与卷积神经网络 (CNN) 进行基准测试.

    主要成果:

    • 该ELM在训练中达到90.65%的准确率,在测试姿势分类时达到89.58%.
    • 精确的分类在动态重雾中被证明高达35厘米.
    • 随着雾密度的增加,ELM和CNN都显示出强度.

    结论:

    • 拟议的基于ELM的框架有效地使用低成本的SPAD传感器在动态,异质的雾中对模型姿势进行分类.
    • 该系统可实现消费电子产品中的实时姿势分类应用.
    • 该研究强调了传感器的局限性和未来的研究方向,以改善光学传感.