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Multi-class Semantic Segmentation of Photovoltaic Module Defects and Features: Towards Industrial Robotic Applications
HANIFI, Shiva; JAFARNEJAD, Sasan; Cormier, Mathieu et al.
2025In Fujita, Hamido (Ed.) Advances and Trends in Artificial Intelligence. Theory and Applications - 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025, Proceedings
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Keywords :
Electroluminescence imaging; Photovoltaic defects; Semantic segmentation; Automated defect detection; Defect class; Defect detection; Industrial robotics; Photovoltaic defect; Photovoltaic modules; Photovoltaics; Robotics applications; Theoretical Computer Science; Computer Science (all)
Abstract :
[en] Automated defect detection in photovoltaic (PV) modules is essential for their maintenance and efficiency, yet challenges such as limited and imbalanced datasets hinder the adoption of high-accuracy systems. This study evaluates six semantic segmentation architectures based on U-Net and SegNet, paired with VGG16, MobileNet, and ResNet50 encoders, and trained on the 29-class dataset of PV module electroluminescence (EL) images. To address dataset imbalance, custom class weights were applied for all the feature and defect classes. VGG16-UNet outperformed other architectures, achieving a mean intersection over union (IoU) of 0.663 for feature classes and 0.326 across defect classes. In particular, it improved the detection of rare defects, such as dead cell, by 0.129 IoU. While previous research focused on a specific subset of classes, this study is the first to provide a comprehensive performance evaluation across all classes. It establishes a baseline for multi-class semantic segmentation in PV defect detection, laying the groundwork for further industrial applications such as in-field defect detection integrated into solar panel cleaning robots. Our implementation is publicly available at https://github.com/sntubix/pv-defect-segmentation, facilitating further research and development.
Disciplines :
Computer science
Author, co-author :
HANIFI, Shiva  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
JAFARNEJAD, Sasan  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
Cormier, Mathieu ;  SolarCleano, Grass, Luxembourg
FRANK, Raphaël  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Ubiquitous and Intelligent Systems (UBI-X)
External co-authors :
no
Language :
English
Title :
Multi-class Semantic Segmentation of Photovoltaic Module Defects and Features: Towards Industrial Robotic Applications
Publication date :
12 July 2025
Event name :
38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Event place :
Kitakyushu, Jpn
Event date :
01-07-2025 => 04-07-2025
Audience :
International
Main work title :
Advances and Trends in Artificial Intelligence. Theory and Applications - 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025, Proceedings
Editor :
Fujita, Hamido
Publisher :
Springer Science and Business Media Deutschland GmbH
ISBN/EAN :
9789819688913
Pages :
37–48
Peer reviewed :
Peer reviewed
Development Goals :
11. Sustainable cities and communities
Name of the research project :
U-AGR-8359 - SOLARCLEANO - FRANK Raphaël
Funders :
SolarCleano
Funding text :
We acknowledge the financial support provided by SolarCleano Company(.3 https://solarcleano.com/en/) for this study.
Available on ORBilu :
since 31 August 2025

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