Enhancing Diameter Measurement Accuracy in Machine Vision Applications

TL;DR

This paper introduces two novel methods to improve diameter measurement accuracy in machine vision systems, significantly reducing errors from micrometers to near negligible levels using reference parts and pixel-based calculations.

Contribution

It presents two innovative approaches—conversion factor-based and pixel-based—for enhancing measurement accuracy with minimal references in machine vision applications.

Findings

Measurement errors reduced from 13-114 micrometers to 1-2 micrometers.

Methods effective across glass and metal samples with diameters 1-24 mm.

High accuracy achieved using few known reference parts.

Abstract

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.