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

Ribonucleic acid binding protein-mediated regulation of luteinizing hormone receptor expression in granulosa cells: relationship to sterol metabolism.

Molecular endocrinology (Baltimore, Md.)·2007
Same author

Psychological stress-induced oxidative stress as a model of sub-healthy condition and the effect of TCM.

Evidence-based complementary and alternative medicine : eCAM·2007
Same author

Overexpression of OsCOIN, a putative cold inducible zinc finger protein, increased tolerance to chilling, salt and drought, and enhanced proline level in rice.

Planta·2007
Same author

Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network.

Bioinformatics (Oxford, England)·2007
Same author

[The value of long-term postoperative follow-up after curative resection of lung cancer and common problems associated with it].

Nihon Geka Gakkai zasshi·2007
Same author

Identification of a type III thioesterase reveals the function of an operon crucial for Mtb virulence.

Chemistry & biology·2007

Related Experiment Video

Updated: Sep 27, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Spark plug defects detection based on improved Faster-RCNN algorithm.

Yuhang Liu1, Yi Liu2, Pengcheng Zhang2

  • 1School of Computer Science and Technology, North University of China, Taiyuan, China.

Journal of X-Ray Science and Technology
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Faster R-CNN model for spark plug defect detection, enhancing accuracy and speed. The new method achieves 89% average accuracy and 97% recall, enabling real-time defect identification.

Keywords:
Defect detectionattention modulefaster-RCNNinceptionV2spark plug

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

662
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

911

Related Experiment Videos

Last Updated: Sep 27, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

662
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

911

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Spark plug defect detection faces challenges with low accuracy and slow processing speeds.
  • Existing deep learning models require optimization for industrial applications.

Purpose of the Study:

  • To enhance spark plug defect detection accuracy and speed using an improved Faster R-CNN model.
  • To develop a robust system for real-time automatic defect identification.

Main Methods:

  • Designed an attention module based symmetrical convolutional network (ASCN) for multi-scale feature extraction.
  • Proposed a multi-scale region generation network (MRPN) using InceptionV2 for varied sliding windows.
  • Established a dataset of 1,402 X-ray spark plug images for training and testing.

Main Results:

  • Achieved 89% average accuracy and 97% recall on the spark plug defect detection task.
  • Outperformed existing models like Faster R-CNN, YOLOv3, SSD, and RetinaNet by over 6% in accuracy and 2% in recall.
  • Demonstrated real-time detection at 20fps with 1024×1024×3 input images.

Conclusions:

  • The improved Faster R-CNN model significantly enhances spark plug defect detection performance.
  • The proposed ASCN backbone and MRPN enable efficient and accurate multi-scale feature extraction.
  • The developed system is suitable for real-time automatic defect detection in industrial settings.