Evidence map›Paper›PMID 39232131›Full record

ArticleScientific reports2024

A two-stage defect detection method for unevenly illuminated self-adhesive printed materials.

Guifeng Peng, Tao Song, Songxiao Cao, Bin Zhou, Qing Jiang

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Guifeng PengCollege of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China.
Tao SongCollege of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China. songtao@cjlu.edu.cn.
Songxiao CaoCollege of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China.
Bin ZhouCollege of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China.
Qing JiangCollege of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China.

Funding

State Administration for Market Regulation of China 2021MK90
6 · The paper itself

Abstract

The process of printing defect detection usually suffers from challenges such as inaccurate defect extraction and localization, caused by uneven illumination and complex textures. Moreover, image difference-based defect detection methods often result in numerous small-scale pseudo defects. To address these challenges, this paper proposes a comprehensive defect detection approach that integrates brightness correction and a two-stage defect detection strategy for self-adhesive printed materials. Concretely, a joint bilateral filter coupled with brightness correction corrects uneven brightness properly, meanwhile smoothing the grid-like texture in complex printed material images. Then, in the first detection stage, an image difference method based on a bright-dark difference template group is designed to effectively locate printing defects despite slight brightness fluctuations. Afterward, a discriminative method based on feature similarity is employed to filter out small-scale pseudo-defects in the second detection stage. The experimental results show that the improved difference method achieves an average precision of 99.1% in defect localization on five different printing pattern samples. Furthermore, the second stage reduces the false detection rate to under 0.5% while maintaining the low missed rate.

Indexed as

Brightness correctionDefect detectionImage differencePrinting defect

Identifiers

PMID39232131
PMCPMC11375033

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.