Cargando…

Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment

Printing defects are extremely common in the manufacturing industry. Although some studies have been conducted to detect printing defects, the stability and practicality of the printing defect detection has received relatively little attention. Currently, printing defect detection is susceptible to...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Xinyu, Li, Yao, Guo, Yiyu, Zhou, Luoyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181735/
https://www.ncbi.nlm.nih.gov/pubmed/37177617
http://dx.doi.org/10.3390/s23094414
_version_ 1785041645707722752
author Liu, Xinyu
Li, Yao
Guo, Yiyu
Zhou, Luoyu
author_facet Liu, Xinyu
Li, Yao
Guo, Yiyu
Zhou, Luoyu
author_sort Liu, Xinyu
collection PubMed
description Printing defects are extremely common in the manufacturing industry. Although some studies have been conducted to detect printing defects, the stability and practicality of the printing defect detection has received relatively little attention. Currently, printing defect detection is susceptible to external environmental interference such as illuminance and noise, which leads to poor detection rates and poor practicality. This research develops a printing defect detection method based on scale-adaptive template matching and image alignment. Firstly, the research introduces a convolutional neural network (CNN) to adaptively extract deep feature vectors from templates and target images at a low-resolution version. Then, a feature map cross-correlation (FMCC) matching metric is proposed to measure the similarity of the feature map between the templates and target images, and the matching position is achieved by a proposed location refinement method. Finally, the matching image and the template are both sent to the image alignment module, so as to detect printing defects. The experimental results show that the accuracy of the proposed method reaches 93.62%, which can quickly and accurately find the location of the defect. Simultaneously, it is also proven that our method achieves state-of-the-art defect detection performance with strong real-time detection and anti-interference capabilities.
format Online
Article
Text
id pubmed-10181735
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-101817352023-05-13 Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment Liu, Xinyu Li, Yao Guo, Yiyu Zhou, Luoyu Sensors (Basel) Article Printing defects are extremely common in the manufacturing industry. Although some studies have been conducted to detect printing defects, the stability and practicality of the printing defect detection has received relatively little attention. Currently, printing defect detection is susceptible to external environmental interference such as illuminance and noise, which leads to poor detection rates and poor practicality. This research develops a printing defect detection method based on scale-adaptive template matching and image alignment. Firstly, the research introduces a convolutional neural network (CNN) to adaptively extract deep feature vectors from templates and target images at a low-resolution version. Then, a feature map cross-correlation (FMCC) matching metric is proposed to measure the similarity of the feature map between the templates and target images, and the matching position is achieved by a proposed location refinement method. Finally, the matching image and the template are both sent to the image alignment module, so as to detect printing defects. The experimental results show that the accuracy of the proposed method reaches 93.62%, which can quickly and accurately find the location of the defect. Simultaneously, it is also proven that our method achieves state-of-the-art defect detection performance with strong real-time detection and anti-interference capabilities. MDPI 2023-04-30 /pmc/articles/PMC10181735/ /pubmed/37177617 http://dx.doi.org/10.3390/s23094414 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Xinyu
Li, Yao
Guo, Yiyu
Zhou, Luoyu
Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment
title Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment
title_full Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment
title_fullStr Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment
title_full_unstemmed Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment
title_short Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment
title_sort printing defect detection based on scale-adaptive template matching and image alignment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181735/
https://www.ncbi.nlm.nih.gov/pubmed/37177617
http://dx.doi.org/10.3390/s23094414
work_keys_str_mv AT liuxinyu printingdefectdetectionbasedonscaleadaptivetemplatematchingandimagealignment
AT liyao printingdefectdetectionbasedonscaleadaptivetemplatematchingandimagealignment
AT guoyiyu printingdefectdetectionbasedonscaleadaptivetemplatematchingandimagealignment
AT zhouluoyu printingdefectdetectionbasedonscaleadaptivetemplatematchingandimagealignment