Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cross-Modal Object Detection Based on Content-Guided Feature Fusion and Self-Calibration.

Sensors (Basel, Switzerland)·2025
Same author

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level.

Sensors (Basel, Switzerland)·2024
Same author

An Infusion Containers Detection Method Based on YOLOv4 with Enhanced Image Feature Fusion.

Entropy (Basel, Switzerland)·2023
Same author

A Novel Hyperchaotic 2D-SFCF with Simple Structure and Its Application in Image Encryption.

Entropy (Basel, Switzerland)·2022
Same author

Quantification and valuation of ecosystem services in life cycle assessment: Application of the cascade framework to rice farming systems.

The Science of the total environment·2020
Same author

Lipid alterations and subtyping maker discovery of lung cancer based on nontargeted tissue lipidomics using liquid chromatography-mass spectrometry.

Journal of pharmaceutical and biomedical analysis·2020

Related Experiment Video

Updated: Jul 30, 2025

Large-area Scanning Probe Nanolithography Facilitated by Automated Alignment and Its Application to Substrate Fabrication for Cell Culture Studies
09:45

Large-area Scanning Probe Nanolithography Facilitated by Automated Alignment and Its Application to Substrate Fabrication for Cell Culture Studies

Published on: June 12, 2018

9.7K

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

Xinyu Liu1, Yao Li1, Yiyu Guo1

  • 1Electronics and Information School, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary

This study introduces a new printing defect detection method using convolutional neural networks (CNNs) and image alignment. The developed technique achieves high accuracy and robust performance against environmental interference for manufacturing quality control.

Keywords:
feature map cross-correlation (FMCC)image alignmentprinting defect detectiontemplate matching

More Related Videos

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

7.7K
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

5.0K

Related Experiment Videos

Last Updated: Jul 30, 2025

Large-area Scanning Probe Nanolithography Facilitated by Automated Alignment and Its Application to Substrate Fabrication for Cell Culture Studies
09:45

Large-area Scanning Probe Nanolithography Facilitated by Automated Alignment and Its Application to Substrate Fabrication for Cell Culture Studies

Published on: June 12, 2018

9.7K
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

7.7K
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

5.0K

Area of Science:

  • Manufacturing Engineering
  • Computer Vision
  • Quality Control

Background:

  • Printing defects are prevalent in manufacturing, yet current detection methods lack stability and practicality.
  • Existing defect detection systems are often compromised by environmental factors like varying illuminance and noise.
  • This leads to reduced detection rates and limited real-world applicability in industrial settings.

Purpose of the Study:

  • To develop a robust and practical printing defect detection method.
  • To enhance the stability and accuracy of defect detection against environmental interference.
  • To improve the real-time detection capabilities for manufacturing quality control.

Main Methods:

  • A convolutional neural network (CNN) is employed for adaptive deep feature extraction from low-resolution images.
  • A novel feature map cross-correlation (FMCC) metric is proposed for template and target image similarity assessment.
  • A location refinement method and image alignment module are integrated for precise defect localization.

Main Results:

  • The proposed method achieved a high detection accuracy of 93.62%.
  • The system demonstrated the ability to quickly and accurately locate printing defects.
  • Experimental validation confirmed state-of-the-art performance, including strong real-time detection and anti-interference capabilities.

Conclusions:

  • The developed scale-adaptive template matching and image alignment method offers a significant advancement in printing defect detection.
  • The approach effectively addresses the limitations of existing methods concerning stability and practicality.
  • This technique provides a reliable solution for enhancing quality control in the manufacturing industry.