A solar cell defect detection model optimized and improved based on YOLOv8

Peng Ziran; Wang Siyuan; Xiao Shenping

doi:10.12086/oee.2024.240220

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Peng Z R, Wang S Y, Xiao S P. A solar cell defect detection model optimized and improved based on YOLOv8[J]. Opto-Electron Eng, 2024, 51(11): 240220. doi: 10.12086/oee.2024.240220

Citation:

Peng Z R, Wang S Y, Xiao S P. A solar cell defect detection model optimized and improved based on YOLOv8[J]. Opto-Electron Eng, 2024, 51(11): 240220. doi: 10.12086/oee.2024.240220

A solar cell defect detection model optimized and improved based on YOLOv8

1.
School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, Hunan 412007, China
2.
Hunan Key Laboratory of Electric Drive Control and Intelligent Equipment, Zhuzhou, Hunan 412007, China

Fund Project: Project supported by National Key Research and Development Program of China (2019YFE0122600), Key Scientific Research Project of the Hunan Provincial Department of Education (22A0423), and Hunan Provincial Natural Science Foundation of China (2023JJ60267, 2022JJ50073)

More Information

^*Corresponding author: pengziran@hut.edu.cn
CSTR: 32245.14.oee.2024.240220

Received Date 16 September 2024

Revised Date 04 November 2024

Accepted Date 04 November 2024

Published Date 25 November 2024

Abstract

Abstract

To address issues of low detection accuracy and high false-positive and false-negative rates in solar cell defect detection, this paper proposes an optimized solar cell electroluminescent (EL) defect detection model based on the YOLOv8 deep learning framework. First, a self-calibrated illumination (SCI) method is applied to preprocess low-light images, enhancing effective feature information for solar cell defects. Then, a space-to-depth (SPD) attention module is introduced, replacing the second stride convolution layer in the backbone network. This substitution avoids information loss caused by stride convolution, expands the receptive field, and reduces computational load, preserving more feature information during extraction. Next, a spatial-BiFPN (S-BFPN) network is constructed to perform multi-scale feature fusion, stabilizing defect recognition rates by addressing the shape variability of solar cell defects. Lastly, the loss function is improved by adopting MPDIoU, which resolves the issue of ineffective penalties in the original CIoU loss function. The experimental results show that the improved YOLOv8 model achieved an mAP of 96.9%, a 2.2% increase compared to the original YOLOv8. The computational load was reduced by 0.2 GFlops, and the detection speed reached a maximum of 155 f/s, demonstrating high accuracy and real-time performance, making it more suitable for industrial deployment.
- deep learning /
- solar cells /
- defect detection /
- YOLOv8

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References

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Overview

Overview

As the global demand for renewable energy grows, solar power has become an essential source of clean energy. However, solar cells often develop defects, such as microcracks, hotspots, and black spots, during production, which significantly impact their conversion efficiency and lifespan. Traditional manual inspection methods are inefficient and limited by lighting conditions, resulting in low detection accuracy with high false-positive and false-negative rates. To meet the need for efficient and precise automated inspection in industrial production, this study aims to develop a high-accuracy, real-time solar cell defect detection model suitable for practical industrial environments. In response, this paper proposes an optimized solar cell electroluminescent (EL) defect detection model based on the YOLOv8 deep learning framework. First, a self-calibrated illumination (SCI) method is applied to preprocess low-light images, enhancing the effective feature information for detecting solar cell defects. Next, a space-to-depth (SPD) attention module is introduced, replacing the second stride convolution layer in the backbone network to prevent information loss caused by stride convolution, expand the receptive field, and reduce computational load, ensuring more comprehensive feature retention. Additionally, a spatial-BiFPN (S-BFPN) network is constructed to perform multi-scale feature fusion, stabilizing recognition rates even when defect shapes vary. Finally, the loss function is improved with the adoption of MPDIoU, addressing the inadequate penalty issues in the original CIoU loss function. Experimental results show that the improved YOLOv8 model achieves an mAP of 96.9%, marking a 2.2% increase over the original YOLOv8, while reducing computational load by 0.2 GFlops. The detection speed reaches a maximum of 155 FPS, demonstrating high accuracy and real-time performance, making it more suitable for industrial applications.