用少量标准件提升机器视觉测径精度,误差从114微米降到2微米。
Enhancing Diameter Measurement Accuracy in Machine Vision Applications
- 通过已知标准件校准,分转换因子和像素直接估计两种方法
- 玻璃与金属样本测径误差由13–114微米降至1–2微米
- 仅需少数参考件,适合工业高精度测径场景
在相机测量系统中,常使用远心镜头等专用设备测量公差极窄的零件。然而,即使采用此类设备,系统内部的机械与软件因素仍可能导致测量误差,尤其在使用相同配置测量不同直径零件时更为明显。本文提出两种创新方法以提升测量精度:基于转换因子的方法,利用已知参考件估算未知零件直径(mm);基于像素的方法,直接从参考件的像素直径信息推算未知件直径(mm)。实验采用工业级相机与远心镜头,对1–12 mm玻璃样品及3–24 mm金属工件进行测试,结果显示原13–114微米的测量误差被降低至1–2微米。仅需少量已知参考件,即可实现视场内所有零件的高精度测量。该方法显著降低误差率,提升测量可靠性,丰富了现有直径测量研究。
原文摘要 · Abstract (English)
In camera measurement systems, specialized equipment such as telecentric lenses is often employed to measure parts with narrow tolerances. However, despite the use of such equipment, measurement errors can occur due to mechanical and software-related factors within the system. These errors are particularly evident in applications where parts of different diameters are measured using the same setup. This study proposes two innovative approaches to enhance measurement accuracy using multiple known reference parts: a conversion factor-based method and a pixel-based method. In the first approach, the conversion factor is estimated from known references to calculate the diameter (mm) of the unknown part. In the second approach, the diameter (mm) is directly estimated using pixel-based diameter information from the references. The experimental setup includes an industrial-grade camera and telecentric lenses. Tests conducted on glass samples (1-12 mm) and metal workpieces (3-24 mm) show that measurement errors, which originally ranged from 13-114 micrometers, were reduced to 1-2 micrometers using the proposed methods. By utilizing only a few known reference parts, the proposed approach enables high-accuracy measurement of all parts within the camera's field of view. Additionally, this method enhances the existing diameter measurement literature by significantly reducing error rates and improving measurement reliability.
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