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Cited by 14Industrial Vision Systems and Defect DetectionAdvanced Neural Network ApplicationsInfrastructure Maintenance and Monitoring

The DOI matches its Crossref record.

Lightweight Convolutional Network For Automated Photovoltaic Defect Detection

Zahid, Arsalan, Hussain, Muhammad, Hill, Richard, Al-Aqrabi, Hussain·2023·2023 9th International Conference on Information Technology Trends (ITT)
DOI 10.1109/itt59889.2023.10184236
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Abstract

As the World moves towards renewable energy, photovoltaic modules are a fundamental option due to their green nature. However, the manufacturing process of solar cells is complex and vulnerable to discrepancies which can impact the overall performance of the system. Although human-led inspection is seen as the de-facto quality inspection protocol, issues pertaining to bias, cost and time can make it an expensive process. To this effect, this paper focuses on the development of a custom convolutional architecture that is lightweight, hence deployable within manufacturing facilities to assist with defective solar cell inspection. In addition, to address the issue of data scarcity, representative data augmentations are producing tailored towards enhancing the model’s generalizability. The high efficacy of the proposed CNN and proposed augmentations can be gauged by the fact that 98% F1 score was achieved overall.

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Crossref · 10.1109/itt59889.2023.10184236

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  • In scenes where objects overlap or are partially obscured, YOLO may struggle to accurately detect and delineate individual instances.

    “In scenarios where objects overlap or are partially obscured [90], YOLO may struggle to accurately detect and delineate individual instances.”

    From YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024) · cites this paper as [90]

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Research works printed but not linked: 23

  1. 10.1109/ACCESS.2022.3178588 · DOI
  2. 10.3390/s22208037 · DOI
  3. 10.1088/1742-6596/1550/3/032050 · DOI
  4. 10.1007/978-3-031-21199-7_12 · DOI
  5. 10.1049/iet-rpg.2019.1342 · DOI
  6. 10.1016/j.solener.2020.03.049 · DOI
  7. 10.1109/ACCESS.2022.3205738 · DOI
  8. 10.1109/ElConRus51938.2021.9396706 · DOI
  9. al-aqrabi, 2020, Hardware intrinsic multi-layer security: a new frontier for 5G enabled IIoT, SENSORS, 20, 1963, 10.3390/s20071963 · DOI
  10. 10.1002/pip.3191 · DOI
  11. al-aqrabi, 2020, Securing manufacturing intelligence for the industrial internet of things, Fourth international Congress in Information and Communication Technology, 267
  12. mahmoud, 2017, Effect of micro cracks on photovoltaic output power: Case study based on real-time long-term data measurements, Micro Nano Lett, 12, 803, 10.1049/mnl.2017.0205 · DOI
  13. mahmoud, 2019, Development of Novel Solar Cell Micro Crack Detection Technique, IEEE Trans Semicond Manuf, 32, 277, 10.1109/TSM.2019.2921951 · DOI
  14. 10.3390/en15228667 · DOI
  15. 10.3934/ElectrEng.2020.1.1 · DOI
  16. 10.15676/ijeei.2021.13.1.14 · DOI
  17. 10.1016/j.solener.2019.02.067 · DOI
  18. 10.3390/s18010209 · DOI · Paper page
  19. 10.3390/en15155492 · DOI
  20. 10.1007/s00138-021-01183-9 · DOI
  21. 10.1016/j.measurement.2016.12.017 · DOI
  22. 10.5194/isprs-archives-XLII-2-893-2018 · DOI
  23. 10.1016/j.solener.2020.01.055 · DOI

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Metadata

Authors
Zahid, A., Hussain, M., Hill, R., & Al-Aqrabi, H.
Affiliations
  • University of Huddersfield (GB)
  • Higher Colleges of Technology (AE)
ORCID
Venue
2023 9th International Conference on Information Technology Trends (ITT)
Publisher
IEEE
Published
2023-05-24
Pages
133-138
DOI
10.1109/itt59889.2023.10184236
OpenAlex
W4385224710
Cited by
14
References
0
Citations by year
2026: 2 · 2025: 3 · 2024: 7 · 2023: 2
FWCI
6.08
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • International Conference on Information Technology Trends (ITT) (publishedVersion)
  • Huddersfield Research Portal (University of Huddersfield) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords
lightweight convolutional neural network, solar cell inspection, automated quality inspection, data augmentation, data scarcity, edge deployment, solar panel manufacturing

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