Evidence map›Paper›PMID 38067720›Full record

ArticleSensors (Basel, Switzerland)2023

Image Segmentation of Fiducial Marks with Complex Backgrounds Based on the mARU-Net.

Xuewei Zhang, Jichun Wang, Yang Wang, Yanwu Feng, Shufeng Tang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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.

Xuewei ZhangState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
Jichun WangState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
Yang WangState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0002-3576-1817
Yanwu FengState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
Shufeng TangInner Mongolia Autonomous Region Special Service Intelligent Robot Key Laboratory, Inner Mongolia University of Technology, Hohhot 010051, China.ORCID 0000-0002-3987-1810

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circuits on different layers in a printed circuit board (PCB) must be aligned according to high-precision fiducial mark images during exposure processing. However, processing quality depends on the detection accuracy of fiducial marks. Precise segmentation of fiducial marks from images can significantly improve detection accuracy. Due to the complex background of PCB images, there are significant challenges in the segmentation and detection of fiducial mark images. In this paper, the mARU-Net is proposed for the image segmentation of fiducial marks with complex backgrounds to improve detection accuracy. Compared with some typical segmentation methods in customized datasets of fiducial marks, the mARU-Net demonstrates good segmentation accuracy. Experimental research shows that, compared with the original U-Net, the segmentation accuracy of the mARU-Net is improved by 3.015%, while the number of parameters and training times are not increased significantly. Furthermore, the centroid method is used to detect circles in segmentation results, and the deviation is kept within 30 microns, with higher detection efficiency. The detection accuracy of fiducial mark images meets the accuracy requirements of PCB production.

Indexed as

CBAMfiducial markmARU-Netresidual blockU-Net

Identifiers

PMID38067720
PMCPMC10708785

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.