LIGHTWEIGHT M-CNN SOLAR CELL SURFACE DEFECT IDENTIFICATION

Tao Zhiyong; Yi Tingjun; Lin Sen; Du Futing

doi:10.19912/j.0254-0096.tynxb.2023-0309

PDF(2871 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (6) : 341-348. DOI: 10.19912/j.0254-0096.tynxb.2023-0309

LIGHTWEIGHT M-CNN SOLAR CELL SURFACE DEFECT IDENTIFICATION

Tao Zhiyong¹, Yi Tingjun¹, Lin Sen², Du Futing¹

Author information +

History +

Abstract

A lightweight micro convolutional neural network （M-CNN） model with high recognition rate is proposed for EL images of solar cells. The network model incorporates a fusion channel attention mechanism to merge multiple feature maps. Introducing Ghost convolutional layers to reduce the model parameters, and using ordinary convolutional layers to replace maximum pooling layers for feature space dimensionality reduction. Experimental results show that on a self-built database of 15767 EL images of cracks, shadows, minor defects, and no defects, M-CNN achieves an accuracy of 99.83% and 93.38% for rough classification and flaw classification detection, respectively, with a model parameter count of 1.29 MB. Notably, compared to advanced networks such as MobileNetV3, DeepVit, and MobileVit, M-CNN has the advantages of superior defect recognition rate and lower model parameter count.

Key words

solar cells / deep learning / convolutional neural networks / image processing / defect identification

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Tao Zhiyong, Yi Tingjun, Lin Sen, Du Futing. LIGHTWEIGHT M-CNN SOLAR CELL SURFACE DEFECT IDENTIFICATION[J]. Acta Energiae Solaris Sinica. 2024, 45(6): 341-348 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0309

References

[1] LI G Q, AKRAM M W, JIN Y, et al.Thermo-mechanical behavior assessment of smart wire connected and busbarPV modules during production, transportation, and subsequent field loading stages[J]. Energy, 2019, 168: 931-945.
[2] 范程华, 王群京, 曹欣远, 等. 基于欠定方程的太阳电池片表面缺陷检测算法[J]. 太阳能学报, 2020, 41(6):288-292.
FAN C H,WANG Q J,CAO X Y,et al.Defect detection algorithm for solar cells surface based on underdetermined equation[J]. Acta energiae solaris sinica, 2020,41(6):288-292.
[3] TSAI D M, LI G N, LI W C, et al.Defect detection in multi-crystal solar cells using clustering with uniformity measures[J]. Advanced engineering informatics, 2015, 29(3): 419-430.
[4] 墨恺, 徐林. 基于阈值均匀局部二值模式和BP神经网络的太阳电池缺陷检测算法[J]. 太阳能学报, 2014, 35(12): 2448-2454.
MO K, XU L.Defect detection algorithm of solar cell based on threshold uniform local binary model and BP neural network[J]. Acta energiae solaris sinica, 2014, 35(12): 2448-2454.
[5] GE C P, LIU Z, FANG L M, et al.A hybrid fuzzy convolutional neural network based mechanism for photovoltaic cell defect detection with electroluminescence images[J]. IEEE transactions on parallel and distributed systems, 2021, 32(7): 1653-1664.
[6] XU C, FAMOURI M, BATHLA G, et al.CellDefectNet: a machine-designed attention condenser network for electroluminescence-based photovoltaic cell defect inspection[C]//2022 19th Conference on Robots and Vision (CRV). Toronto, ON, Canada, 2022: 219-223.
[7] TANG W Q, YANG Q, XIONG K X, et al.Deep learning based automatic defect identification of photovoltaic module using electroluminescence images[J]. Solar energy, 2020, 201: 453-460.
[8] DEITSCH S, CHRISTLEIN V, BERGER S, et al.Automatic classification of defective photovoltaic module cells in electroluminescence images[J]. Solar energy, 2019, 185: 455-468.
[9] WOO S, PARK J, LEE J Y, et al.CBAM: convolutional block attention module[C]//European Conference on Computer Vision. Cham: Springer, 2018: 3-19.
[10] HAN K, WANG Y H, TIAN Q, et al.GhostNet: more features from cheap operations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA, 2020: 1577-1586.
[11] HOWARD A, SANDLER M, CHEN B, et al.Searching for MobileNetV3[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South), 2019: 1314-1324.
[12] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA, 2018: 7132-7141.
[13] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 2016: 770-778.
[14] SZEGEDY C, VANHOUCKE V, IOFFE S, et al.Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas, NV, USA, 2016: 2818-2826.
[15] YUAN L, CHEN Y P, WANG T, et al.Tokens-to-token ViT: training vision transformers from scratch on ImageNet[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada, 2021: 538-547.
[16] HAN K, XIAO A, WU E, et al.Transformer in transformer[J]. Advances in neural information processing systems, 2021, 34: 15908-15919.
[17] ZHOU D Q, KANG B Y, JIN X J, et al. DeepViT: towards deeper vision transformer[EB/OL].2021: arXiv: 2103.11886. http://arxiv.org/abs/2103.11886.
[18] MEHTA S, RASTEGARI M. MobileViT: light-weight, general-purpose,mobile-friendly vision transformer[EB/OL].2021: arXiv: 2110.02178. http://arxiv.org/abs/2110.02178.
[19] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA, 2017: 2261-2269.
[20] TAN M X, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL].2019: arXiv: 1905.11946. http://arxiv.org/abs/1905.11946.