Version 1
: Received: 27 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (13:34:52 CEST)
How to cite:
Aktouf, L.; Shivanna, Y.; Dhimish, M. High‐Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model. Preprints2024, 2024072435. https://doi.org/10.20944/preprints202407.2435.v1
Aktouf, L.; Shivanna, Y.; Dhimish, M. High‐Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model. Preprints 2024, 2024072435. https://doi.org/10.20944/preprints202407.2435.v1
Aktouf, L.; Shivanna, Y.; Dhimish, M. High‐Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model. Preprints2024, 2024072435. https://doi.org/10.20944/preprints202407.2435.v1
APA Style
Aktouf, L., Shivanna, Y., & Dhimish, M. (2024). High‐Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model. Preprints. https://doi.org/10.20944/preprints202407.2435.v1
Chicago/Turabian Style
Aktouf, L., Yathin Shivanna and Mahmoud Dhimish. 2024 "High‐Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model" Preprints. https://doi.org/10.20944/preprints202407.2435.v1
Abstract
This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images, annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIB) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision ([email protected]) of 98.5%. Detailed analysis of the model's performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as thick line and star crack, the model maintained high performance with accuracies of 94% and 96%, respectively. The recall-confidence and precision-recall curves further demonstrate the model's robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production.
Keywords
YOLOv10; Object detection; Deep learning; Photovoltaic manufacturing
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
