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

XLSTM transformer based quality prediction for tobacco cut rag in intermittent processing.

Scientific reports·2026
Same author

Feasibility and comparability in pediatric donation after circulatory death kidney transplantation.

Pediatric nephrology (Berlin, Germany)·2026
Same author

Ultrasound-programmable tumor extracellular vesicles delivery system for dual immune-epigenetic therapy against colorectal cancer metastasis.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Interpreting 1-year quality-of-life findings after colon cancer surgery in older adults with frailty.

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland·2026
Same author

Insomnia Pathogenesis and Multidimensional Mechanisms of Acupuncture: A Narrative Review.

International journal of general medicine·2026
Same author

Low-intensity focused ultrasound-activated piezoelectric gel bandage for diabetic wound repair and neuropathic pain relief.

Nature communications·2026

Related Experiment Video

Updated: Jul 5, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.1K

Steel Strip Surface Defect Detection Method Based on Improved YOLOv5s.

Jianbo Lu1, Mingrui Zhu1, Xiaoya Ma2

  • 1Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning 530001, China.

Biomimetics (Basel, Switzerland)
|January 22, 2024
PubMed
Summary
This summary is machine-generated.

An improved YOLOv5s model, YOLOv5s-FPD, enhances steel strip surface defect detection. This fine-grained defect detection model shows improved accuracy on benchmark datasets, boosting quality control in manufacturing.

Keywords:
CBAMCSBLSPPF-AYOLOv5sstrip surface defect detection

More Related Videos

Production of a Strain-Measuring Device with an Improved 3D Printer
06:17

Production of a Strain-Measuring Device with an Improved 3D Printer

Published on: January 30, 2020

6.2K
Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

13.8K

Related Experiment Videos

Last Updated: Jul 5, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.1K
Production of a Strain-Measuring Device with an Improved 3D Printer
06:17

Production of a Strain-Measuring Device with an Improved 3D Printer

Published on: January 30, 2020

6.2K
Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope
11:14

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

13.8K

Area of Science:

  • Materials Science
  • Computer Vision
  • Industrial Engineering

Background:

  • Steel strip production is crucial for major industries, but surface defects like cracks and pitting reduce quality.
  • Machine vision is vital for detecting these defects, yet fine-grained feature classification remains challenging.
  • Existing methods struggle with accurate and efficient detection of subtle surface imperfections.

Purpose of the Study:

  • To develop an advanced machine vision model for accurate steel strip surface defect detection.
  • To improve the classification of fine-grained features in strip steel surface images.
  • To enhance the overall defect detection rate and quality control in steel strip manufacturing.

Main Methods:

  • An improved YOLOv5s model, termed YOLOv5s-FPD (Fine Particle Detection), was proposed.
  • Key modules integrated include SPPF-A for spatial pyramid adjustment, ASFF and CARAFE for feature fusion, and CSBL and DCNv2 for lightweight properties.
  • The Convolutional Block Attention Module (CBAM) was incorporated for enhanced feature extraction.

Main Results:

  • YOLOv5s-FPD achieved a 2.6% mAP50 improvement over YOLOv5s on the NEU_DET dataset before enhancement.
  • Post-SSIE data enhancement, the model showed an 1.8% mAP50 increase on NEU_DET.
  • Significant accuracy improvements were observed for all six defect types in the NEU_DET dataset and a 4.6% mAP50 increase on the VOC2007 dataset.

Conclusions:

  • The proposed YOLOv5s-FPD model effectively addresses challenges in fine-grained steel strip surface defect detection.
  • The integration of advanced modules significantly enhances feature extraction, fusion, and detection accuracy.
  • The model demonstrates superior performance and validity for industrial applications requiring high-quality surface inspection.