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: May 14, 2026

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

MSW-Mamba-Det: Multi-Scale Windowed State-Space Modeling for End-to-End Defect Detection in Photovoltaic Module

Xiaofeng Wang1, Haojie Hu1, Xiao Hao2

  • 1School of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030006, China.

Sensors (Basel, Switzerland)
|May 13, 2026
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

Case Report: A case of acute anterior wall myocardial infarction caused by stent fracture in the left anterior descending artery.

Frontiers in cardiovascular medicine·2026
Same author

Self-heterodyne spectroscopy via a non-uniformly spaced frequency comb.

Nature communications·2026
Same author

Cross-instrument coal quality spectral analysis via a PDS-assisted fine-tuning calibration transfer method.

Analytica chimica acta·2026
Same author

A single-cell multi-omics atlas of human eyelid skin.

Frontiers in genetics·2026
Same author

Development and validation of a multidimensional indicator-based risk prediction model for gestational diabetes mellitus: a nested case-control study.

BMC endocrine disorders·2026
Same author

Bridging Population Patterns and Individual Prediction: Framework for Prospective Multimorbidity Study.

JMIR medical informatics·2026
This summary is machine-generated.

A new defect detection framework, MSW-Mamba-Det, improves photovoltaic (PV) electroluminescence (EL) imaging analysis. It accurately identifies low-contrast defects in complex backgrounds, enhancing solar panel inspection efficiency.

Area of Science:

  • Materials Science
  • Computer Vision
  • Renewable Energy Technology

Background:

  • Electroluminescence (EL) imaging is crucial for photovoltaic (PV) module inspection.
  • Detecting defects in EL images is challenging due to low contrast and complex background patterns.
  • Existing methods struggle with high-resolution inputs and subtle defect identification.

Purpose of the Study:

  • To develop an advanced defect detection framework for PV EL imaging.
  • To address the limitations of current methods in identifying low-contrast and background-obscured defects.
  • To improve the accuracy and robustness of automated PV module inspection.

Main Methods:

  • Proposed MSW-Mamba-Det, an end-to-end framework built upon RT-DETR.
  • Introduced MSW-Mamba module with Local/Stripe/Grid architecture for multi-scale dependency modeling.
Keywords:
defect detectionelectroluminescence imagingend-to-end detectionphotovoltaic modulesstate-space model

More Related Videos

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Related Experiment Videos

Last Updated: May 14, 2026

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

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

  • Incorporated DetailAware module for high-frequency texture restoration and adaptive gating.
  • Utilized Pyramid Attention Fusion Block (PAFB) for enhanced multi-scale feature alignment and fusion.

    • Main Results:

    • MSW-Mamba-Det achieved AP50:95 scores of 60.4% on PV-Multi-Defect-main and 68.0% on PVEL-AD.
    • Demonstrated significant improvements over the baseline RT-DETR by 2.5 and 2.2 points, respectively.
    • Outperformed 12 other representative models, excelling in detecting medium and large defects.

    Conclusions:

    • The proposed MSW-Mamba-Det framework effectively enhances PV EL defect inspection.
    • The novel modules successfully address challenges posed by low-contrast defects and structured backgrounds.
    • MSW-Mamba-Det offers a robust solution for automated quality control in solar panel manufacturing.