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2024 | OriginalPaper | Chapter

Efficient Lightweight Network with Transformer-Based Distillation for Micro-crack Detection of Solar Cells

Authors : Xiangying Xie, Xinyue Liu, QiXiang Chen, Biao Leng

Published in: Neural Information Processing

Publisher: Springer Nature Singapore

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Abstract

Micro-cracks on solar cells often affect the power generation efficiency, so this paper proposes a lightweight network for cell image micro-crack detection task. Firstly, a Feature Selection framework is proposed, which can efficiently and adaptively decide the number of layers of the feature extraction network, and clip unnecessary feature generation process. In addition, based on the design of the Transformer layer, Transformer Distillation is proposed. In Transformer Distillation, the designed Transformer Refine module excavates the distillation information from the two dimensions of features and relations. Using a combination of Feature Selection and Transformer Distillation, the lightweight networks based on ResNet and ViT can achieve much better effects than the original networks, with classification accuracy rates of 88.58% and 89.35% respectively.

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next chapter MTLAN: Multi-Task Learning and Auxiliary Network for Enhanced Sentence Embedding
Literature
1.
Anwar, S.A., Abdullah, M.Z.: Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique. EURASIP J. Image Video Process. 2014, 1–17 (2014)CrossRef
2.
Buerhop-Lutz, C., et al.: A benchmark for visual identification of defective solar cells in electroluminescence imagery. In: 35th European PV Solar Energy Conference and Exhibition, vol. 12871289, pp. 1287–1289 (2018)
3.
Deitsch, S., et al.: Segmentation of photovoltaic module cells in uncalibrated electroluminescence images. Mach. Vis. Appl. 32(4), 84 (2021)CrossRef
4.
Deitsch, S., et al.: Automatic classification of defective photovoltaic module cells in electroluminescence images. Sol. Energy 185, 455–468 (2019)CrossRef
5.
Demirci, M.Y., Beşli, N., Gümüşçü, A.: Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in electroluminescence images. Expert Syst. Appl. 175, 114810 (2021)CrossRef
6.
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:​2010.​11929 (2020)
7.
Fuyuki, T., Kitiyanan, A.: Photographic diagnosis of crystalline silicon solar cells utilizing electroluminescence. Appl. Phys. A 96, 189–196 (2009)CrossRef
8.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
9.
10.
Johnston, S., et al.: Correlating multicrystalline silicon defect types using photoluminescence, defect-band emission, and lock-in thermography imaging techniques. IEEE J. Photovoltaics 4(1), 348–354 (2013)CrossRef
11.
Mirzadeh, S.I., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., Ghasemzadeh, H.: Improved knowledge distillation via teacher assistant. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5191–5198 (2020)
12.
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. arXiv preprint arXiv:​1412.​6550 (2014)
13.
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
14.
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357. PMLR (2021)
15.
Tsai, D.M., Wu, S.C., Li, W.C.: Defect detection of solar cells in electroluminescence images using Fourier image reconstruction. Sol. Energy Mater. Sol. Cells 99, 250–262 (2012)CrossRef
16.
Tseng, D.C., Liu, Y.S., Chou, C.M.: Automatic finger interruption detection in electroluminescence images of multicrystalline solar cells. Math. Prob. Eng. 2015 (2015)
17.
Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:​1612.​03928 (2016)
18.
Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K.: Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3713–3722 (2019)
19.
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320–4328 (2018)
Metadata
Title
Efficient Lightweight Network with Transformer-Based Distillation for Micro-crack Detection of Solar Cells
Authors
Xiangying Xie
Xinyue Liu
QiXiang Chen
Biao Leng
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Neural Information Processing

Print ISBN: 978-981-9980-66-6

Electronic ISBN: 978-981-9980-67-3

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-99-8067-3_1

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