多尺度自适应光条中心提取线结构光视觉基于在线轮架测量
Saisai Liu1,2, Qixin He1,2, Wenjie Fu1,2
1School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新的区域适应的多尺度方法,用于在火车轮胎面测量中准确地提取光条中心. 该技术提高了工业应用的稳定性和精度.
科学领域:
- 计算机视觉 计算机视觉
- 计量学 计量学 计量学
- 工业检查 工业检查 工业检查
背景情况:
- 精确的光条中心提取对于线结构光视测量至关重要.
- 复杂的火车轮流面形状导致灰度水平分布不均和条纹宽度变化,降低了测量准确度.
- 现有的方法难以应对火车轮流程的具有挑战性的表面特性.
研究的目的:
- 提出一种新的适应区域的多尺度方法,用于强大的轻条纹中心提取.
- 为了应对在线测量中多样化和复杂的火车轮流面形状所带来的挑战.
- 提高工业测量系统的稳定性和准确性.
主要方法:
- 使用灰色梯度特征进行图像细分,以识别和增强潜在的光条区域.
- 适应性确定每个区域的最佳尺度参数 (σ) 使用规范化的高斯核响应.
- 通过Hessian矩阵与最佳尺度参数的子像素光条中央提取.
主要成果:
- 拟议的方法可靠地提取轻条纹中心,在不均的车轮表面具有高稳定性.
- 实现了0.10毫米的重复性,用于轻的条纹中心提取.
- 已证明的平均测量误差为0.12mm的边缘高度和0.10mm的边缘厚度.
结论:
- 适应区域的多尺度方法显著提高了轻条纹中心提取的稳定性和准确性.
- 该技术已被验证用于工业测量环境,特别是用于火车轮流面分析.
- 该方法的可重复性和可重复性通过广泛的测试得到证实.
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