Evidence map›Paper›PMID 42356673›Full record

ArticleSensors (Basel, Switzerland)2026

Machine Vision-Based Precision Detection of Circular Holes Using Canny Threshold Optimization and Zernike Moments.

Juan Du, Jizheng Yu, Xintian Jiang, Xiaorui Li, Xiaodong Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Juan DuSchool of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Jizheng YuSchool of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Xintian JiangSchool of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Xiaorui LiSchool of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Xiaodong LiuSchool of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252000, China.ORCID 0000-0001-7545-2253

Funding

Liaocheng University 318052058the Innovation Capability Enhancement Initiative for Technology-Oriented SMEs of Shandong Province 2024PDJH38/K24LB9601
6 · The paper itself

Abstract

This study proposes a precision detection method that integrates Canny operator threshold optimization with Zernike moments to address the issue of low measurement accuracy associated with the manual inspection of circular holes in sheet metal during industrial testing. A complete automated measurement system was developed based on the MATLAB platform. First, adaptive median filtering is employed for image preprocessing, with superior performance in noise suppression and detail preservation validated through Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics. Subsequently, Otsu's thresholding method achieves robust segmentation between target and background, laying the foundation for subsequent edge detection. An innovative adaptive threshold selection strategy for the Canny operator based on composite weight scoring was proposed during edge detection, significantly enhancing circular hole edges' continuity and geometric integrity. Finally, by integrating Zernike moments with sub-pixel localization technology, ultra-precise localization of edge points at the sub-pixel level was achieved. Experimental results demonstrate that the system achieves a measurement repeatability standard deviation of less than 0.02 mm and controls the absolute error within ±0.05 mm.This performance surpasses the ±0.3 mm precision requirement in industrial settings, providing an effective solution for automated quality inspection of sheet metal hole manufacturing.

Indexed as

algorithm optimizationCanny operatorcircular hole detectionsubpixelZernike moments

Identifiers

PMID42356673
PMCPMC13306455

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.