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 Experiment Video

Updated: Jan 7, 2026

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

990

Printed Circuit Board Defect Detection Based on Lightweight Deep Learning Fusion Model.

Yuling Wang1, Zhicheng Chen2, Jie Wang1

  • 1School of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

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

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Scientific reports·2026
Same author

Medical hierarchical image classification via dual-geometry image-text learning.

Medical image analysis·2026
Same author

Multiple attention based deep multimodal fusion network for glaucoma and neurodegenerative disease diagnosis.

Scientific reports·2026
Same author

LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Multiscale attention generative adversarial networks for lesion synthesis in chest X-ray images.

Scientific reports·2025
Same author

GRAPHITE: Graph-based interpretable tissue examination for enhanced explainability in breast cancer histopathology.

Computers in biology and medicine·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

This study introduces an improved model for detecting tiny defects on printed circuit boards (PCBs). The novel approach enhances detection accuracy and speed, benefiting PCB manufacturing industries.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Manufacturing Technology

Background:

  • Printed circuit boards (PCBs) are critical electronic components requiring high-precision defect detection.
  • Existing methods struggle with identifying tiny defects on PCBs, impacting manufacturing quality.

Purpose of the Study:

  • To develop an improved model for tiny defect detection on PCBs.
  • To enhance model performance through compression and advanced feature representation.

Main Methods:

  • Proposed a compact model based on MobileNet v3 Small-CA with an image-cutting layer.
  • Implemented an improved multi-scale fusion with a location-weighted mechanism.
  • Evaluated the model on a public synthetic PCB dataset.
Keywords:
PCBdefect detectionfeature fusionlightweight deep learning model

Related Experiment Videos

Last Updated: Jan 7, 2026

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

990

Main Results:

  • The proposed model demonstrated superior performance compared to state-of-the-art algorithms like Faster R-CNN, EfficientDet, SSD, and YOLO v7.
  • Achieved enhanced detection accuracy and speed for tiny object detection on PCBs.

Conclusions:

  • The developed model offers significant improvements for PCB defect detection, particularly for tiny defects.
  • The enhanced speed and accuracy benefit the PCB manufacturing industry, leading to better quality control.