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通过多尺度混合内核和结构重新参数化,有效地识别光学元件的表面缺陷.

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    科学领域:

    • 光学和材料科学 材料科学
    • 计算机视觉和机器学习

    背景情况:

    • 光学表面缺陷的识别对于制造中的质量控制至关重要.
    • 挑战包括缺陷的微妙性,复杂性和规模的变化.

    研究的目的:

    • 开发一种高效准确的深度学习模型,用于识别各种光学表面缺陷.
    • 解决现有方法在处理各种缺陷特征方面的局限性.

    主要方法:

    • 一个深度网络,采用多级混合内核来捕获不同受体场的特征.
    • 一个不对称的混合内核设计,用于旋转强大的特征提取.
    • 结构重新参数化,以优化模型的快速推理.

    主要成果:

    • 拟议的方法在光学表面缺陷数据集上实现了97.39%的准确性.
    • 推断速度达到201.76/秒,只有5.23M的参数.
    • 在识别制造和非制造缺陷方面表现出卓越的性能.

    结论:

    • 开发的深度网络为光学表面缺陷识别提供了有效的解决方案.
    • 该模型的精度和速度平衡符合实际的工业要求.
    • 结构重新参数化提高了部署能力,而不会影响性能.