CSA-ASPP-Net:端到端的激光条纹中线提取与联合通道空间注意力和的空间金字塔卷积
Applied optics
|September 22, 2025
概括
本研究介绍了CSA-ASPP-Net,这是一个端到端的深度学习模型,用于精确的激光条纹中线提取. 它实现了高精度和速度,改进了结构化的光测量系统.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 光学计量学 在光学计量学
背景情况:
- 传统的激光条纹中线提取依赖于手动功能,限制了准确性和适应性.
- 现有的深度学习方法通常需要多阶段处理 (细分然后提取).
研究的目的:
- 开发一个集成的,端到端的深度学习模型,用于直接激光条纹中心线提取.
- 为了提高结构光测量系统的精度和效率.
主要方法:
- 拟议的CSA-ASPP-Net模型整合了Atrous空间金字塔聚合 (ASPP) 和通道空间注意 (CBAM) 模块.
- 一个编码器-解码器架构,用于单阶段条纹本地化和改进.
- 注意引导的特征增强和多尺度的上下文感知.
主要成果:
- 在测试图像上获得了94.85%的精度和0.64像素的平均定位误差.
- 证明了计算效率,每张图像的处理速度为0.15秒.
- CBAM模块有效地减少了背景干扰;ASPP模块提高了适应各种条纹形状的适应性.
结论:
- 端到端的CSA-ASPP-Net为结构光系统中的激光条纹处理提供了一个创新的解决方案.
- 该模型将高精度与计算效率相结合,性能优于传统和现有的深度学习方法.
- 集成的注意力和多尺度模块提高了对现实世界的应用的稳定性和准确性.
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