IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i23p7726-d1286035.html
   My bibliography  Save this article

SEiPV-Net: An Efficient Deep Learning Framework for Autonomous Multi-Defect Segmentation in Electroluminescence Images of Solar Photovoltaic Modules

Author

Listed:
  • Hassan Eesaar

    (Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
    These authors equally contributed to this work.)

  • Sungjin Joe

    (Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of Korea
    These authors equally contributed to this work.)

  • Mobeen Ur Rehman

    (Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi 127788, United Arab Emirates)

  • Yeongmin Jang

    (Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Kil To Chong

    (Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
    Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea)

Abstract

A robust and efficient segmentation framework is essential for accurately detecting and classifying various defects in electroluminescence images of solar PV modules. With the increasing global focus on renewable energy resources, solar PV energy systems are gaining significant attention. The inspection of PV modules throughout their manufacturing phase and lifespan requires an automatic and reliable framework to identify multiple micro-defects that are imperceptible to the human eye. This manuscript presents an encoder–decoder-based network architecture with the capability of autonomously segmenting 24 defects and features in electroluminescence images of solar photovoltaic modules. Certain micro-defects occupy a trivial number of image pixels, consequently leading to imbalanced classes. To address this matter, two types of class-weight assignment strategies are adopted, i.e., custom and equal class-weight assignments. The employment of custom class weights results in an increase in performance gains in comparison to equal class weights. Additionally, the proposed framework is evaluated by utilizing three different loss functions, i.e., the weighted cross-entropy, weighted squared Dice loss, and weighted Tanimoto loss. Moreover, a comparative analysis based on the model parameters is carried out with existing models to demonstrate the lightweight nature of the proposed framework. An ablation study is adopted in order to demonstrate the effectiveness of each individual block of the framework by carrying out seven different experiments in the study. Furthermore, SEiPV-Net is compared to three state-of-the-art techniques, namely DeepLabv3+, PSP-Net, and U-Net, in terms of several evaluation metrics, i.e., the mean intersection over union (IoU), F1 score, precision, recall, IoU, and Dice coefficient. The comparative and visual assessment using SOTA techniques demonstrates the superior performance of the proposed framework.

Suggested Citation

  • Hassan Eesaar & Sungjin Joe & Mobeen Ur Rehman & Yeongmin Jang & Kil To Chong, 2023. "SEiPV-Net: An Efficient Deep Learning Framework for Autonomous Multi-Defect Segmentation in Electroluminescence Images of Solar Photovoltaic Modules," Energies, MDPI, vol. 16(23), pages 1-16, November.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7726-:d:1286035
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/23/7726/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/23/7726/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7726-:d:1286035. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.