Enhancing Diameter Measurement Accuracy in Machine Vision Applications

2025-07-28

AI Summary

In telecentric lens-based vision measurement systems, diameter measurement errors—induced by mechanical misalignments and software imperfections—exhibit strong dependence on the nominal diameter of the inspected part. To address this, this paper proposes two high-precision calibration methods requiring only a small set of reference parts with known diameters: the conversion factor method and the pixel-level diameter estimation method. Both leverage regression modeling and optimized unit conversion to achieve full-field measurement error compensation without expensive metrological equipment. Experimental validation on glass (1–12 mm) and metal (3–24 mm) components demonstrates that measurement errors are reduced from 13–114 μm to 1–2 μm—a 98% improvement in accuracy. The proposed approaches significantly enhance system robustness and practical deployability, establishing a new paradigm for cost-effective, high-precision industrial vision metrology.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Improving diameter measurement accuracy in machine vision systems
• Reducing errors caused by mechanical and software factors
• Enhancing reliability for parts of varying diameters

Innovation

• Uses multiple known reference parts
• Employs conversion factor-based method
• Implements pixel-based diameter estimation