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

Lumber Defects01:23

Lumber Defects

818
Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
818

You might also read

Related Articles

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

Sort by
Same author

Downregulation of RUNX3 has a poor prognosis and promotes tumor progress in kidney cancer.

Urologic oncology·2020
Same author

Physicochemical and emulsifying properties of mussel water-soluble proteins as affected by lecithin concentration.

International journal of biological macromolecules·2020
Same author

Colchicine prevents atrial fibrillation promotion by inhibiting IL-1β-induced IL-6 release and atrial fibrosis in the rat sterile pericarditis model.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2020
Same author

Solid Polymer Electrolytes with Flexible Framework of SiO<sub>2</sub> Nanofibers for Highly Safe Solid Lithium Batteries.

Polymers·2020
Same author

Chemerin facilitates intervertebral disc degeneration via TLR4 and CMKLR1 and activation of NF-kB signaling pathway.

Aging·2020
Same author

Multivariate analysis reveals effect of glutathione-enriched inactive dry yeast on amino acids and volatile components of kiwi wine.

Food chemistry·2020

Related Experiment Video

Updated: May 3, 2026

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages
08:46

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Published on: April 13, 2016

10.5K

MDEB-YOLO: A Lightweight Multi-Scale Attention Network for Micro-Defect Detection on Printed Circuit Boards.

Xun Zuo1, Ning Zhao1, Ke Wang2

  • 1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

Micromachines
|February 27, 2026
PubMed
Summary

This study introduces MDEB-YOLO, a novel deep learning model for detecting micro-defects on Printed Circuit Boards (PCBs). It achieves high accuracy and speed, outperforming existing methods for crucial industrial quality control.

Keywords:
MDEB-YOLOPCB defect detectionefficient multi-scale deformable attention (EMDA)feature pyramid networklightweight network

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.9K

Related Experiment Videos

Last Updated: May 3, 2026

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages
08:46

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Published on: April 13, 2016

10.5K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.9K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Electronics Manufacturing

Background:

  • Printed Circuit Board (PCB) defect detection is vital for electronics quality control.
  • Existing deep learning models struggle with small, irregular defects and background noise, balancing accuracy and speed.
  • Challenges include intricate circuitry, minute defect scales, and varied morphologies.

Purpose of the Study:

  • To develop a lightweight, real-time detection network (MDEB-YOLO) for PCB micro-defects.
  • To enhance the model's ability to perceive subtle geometric variations and extract irregular defect features.
  • To improve the representation of small targets and reduce computational complexity for industrial applications.

Main Methods:

  • Proposed the Efficient Multi-scale Deformable Attention (EMDA) module for enhanced feature extraction.
  • Introduced a Bidirectional Residual Multi-scale Feature Pyramid Network (BRM-FPN) to mitigate feature loss.
  • Developed a Lightweight Grouped Convolution Head (LGC-Head) to reduce model size and complexity.

Main Results:

  • MDEB-YOLO achieved 95.9% mAP and 80.6 FPS on the PKU-Market-PCB dataset.
  • Demonstrated a 1.5% mAP improvement and 26.5% faster inference compared to baseline models.
  • Significantly improved detection accuracy for mouse bite (3.7%) and spur (4.0%) defects.

Conclusions:

  • MDEB-YOLO offers superior accuracy and real-time performance for PCB micro-defect detection.
  • The proposed EMDA, BRM-FPN, and LGC-Head modules effectively address challenges in detecting small, irregular defects.
  • The model holds significant value for industrial electronics manufacturing quality control.