Sub-Pixel counting based diameter measurement algorithm for industrial Machine vision

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dc.contributor.authorPoyraz, Ahmet Gökhan
dc.contributor.authorKaçmaz, Mehmet Ali
dc.contributor.authorGürkan, Hakan
dc.contributor.authorDirik, Ahmet Emir
dc.date.accessioned2026-02-08T15:11:10Z
dc.date.available2026-02-08T15:11:10Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn recent years, there has been a notable surge in the utilization of industrial image processing applications across various sectors, including automotive, medical, and space industries. These applications rely on specialized camera systems and advanced image processing techniques to accurately measure working products with precise tolerances. This research presents a novel fast algorithm for measuring the diameter of a ring, employing a subpixel counting method. The algorithm classifies image pixels into two categories: full pixels and transition pixels. Full pixels reside entirely within the inner region of the workpiece, while transition pixels represent gray pixels that reside at the boundary between the workpiece and its background. To ensure accurate determination of the object area, the proposed method incorporates normalization to account for the contribution of transition pixels alongside full pixels. Subsequently, the circle area equation is employed to calculate the diameter. Moreover, a robust threshold selection method is introduced to effectively distinguish pixels with gray intensities. The experimental setup consists of an industrial camera equipped with telecentric lenses and appropriate illumination. The results demonstrate that the proposed algorithm achieves a 3–10 % improvement in accuracy compared to existing approaches. In terms of measuring sensitivity, the operational sensitivity of the proposed methodology is quantified as 1/20th of the pixel size, exhibiting an average uncertainty of 1 µm. Furthermore, the proposed method surpasses existing works by at least 12.5 % to 35 % in terms of benchmarking computing time. © 2023 Elsevier Ltd
dc.identifier.doi10.1016/j.measurement.2023.114063
dc.identifier.issn0263-2241
dc.identifier.scopus2-s2.0-85183748871
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2023.114063
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5275
dc.identifier.volume225
dc.identifier.wosWOS:001153325800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofMeasurement: Journal of the International Measurement Confederation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_KA_20260207
dc.subjectDiameter measurement
dc.subjectImage processing
dc.subjectIndustrial machine vision
dc.subjectO-ring
dc.subjectRadius
dc.subjectSubpixel
dc.titleSub-Pixel counting based diameter measurement algorithm for industrial Machine vision
dc.typeArticle

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