Work

A subpixel vision metrology for industrial workpiece diameter estimation

Year: 2026

Type: article

Abstract: Accurate non-contact diameter measurement is essential for industrial inspection. This study introduces OneShot-SPRDN, a one-shot vision-metrology framework combining support-guided boundary localization, subpixel offset prediction and checkerboard-based calibration. The framework uses RGB, mask and masked-image inputs to reconstruct subpixel contours and estimate rod diameters. Evaluation includes an original $$0^\circ $$ reference image and four additional images of two metallic workpieces with varying diameters, illumination and camera-framing conditions. For the reference image, OneShot-SPRDN estimates a diameter of $$28.243~\textrm{mm}$$ , compared with the coordinate measuring machine (CMM) reference of $$28.237~\textrm{mm}$$ , yielding a mean absolute error (MAE) of $$6.000~\mu \textrm{m}$$ . The additional evaluations show agreement with CMM measurements across different workpiece geometries and imaging conditions, supporting the framework’s potential for industrial dimensional inspection.

Source: The International Journal of Advanced Manufacturing Technology

DOI: 10.1007/s00170-026-19443-1

Authors: Ahmad Junaid, Muhammad Abeer Irfan, Muftooh ur Rehman Siddiqi, Umar Riaz, Abuzar Khan

Institutions: CECOS University, University of Engineering and Technology Peshawar, Technological University Dublin, Aston University

Language: English

Topic: Optical measurement and interference techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,990

OpenAlex ID: W7225397905

FWCI:

Cites: 66

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Related to: 10