Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (2): 458-467.DOI: 10.3778/j.issn.1673-9418.2111036

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Micro-cracks Detection of Solar Cells Based on Few Shot Samples with Multi-loss

NA Zhixiong1,+(), FAN Tao2, SUN Tao1, XIE Xiangying3,1, LAI Guangzhi1   

  1. 1. State Grid Electronic Commerce Co., Ltd., Beijing 100053, China
    2. State Grid Corporation of China, Beijing 100031, China
    3. Beihang University, Beijing 100191, China
  • Received:2021-11-05 Revised:2022-01-12 Online:2022-02-01 Published:2022-01-17
  • About author:NA Zhixiong, born in 1974, M.S., engineer. His research interests include renewable energy, new power system, etc.
    FAN Tao, born in 1971, M.S., professor of engineering. His research interests include enterprise informatization, power system automation, energy Internet, etc.
    SUN Tao, born in 1968, senior engineer. His research interests include power system automation, energy industry Internet, etc.
    XIE Xiangying, born in 1979, Ph.D. candidate, senior engineer. His research interest is industry Internet.
    LAI Guangzhi, born in 1969, M.S., professor of economist. His research interests include new energy development and consumption management, etc.
  • Supported by:
    National Key Research and Development Program of China(2018YFB1500800);Technology Project of State Grid Corporation of China(SGTJDK00DYJS2000148)


那峙雄1,+(), 樊涛2, 孙涛1, 谢祥颖3,1, 来广志1   

  1. 1.国网电子商务有限公司,北京 100053
    2.国家电网有限公司,北京 100031
    3.北京航空航天大学,北京 100191
  • 通讯作者: + E-mail:
  • 作者简介:那峙雄(1974—),男,北京人,硕士,工程师,主要研究方向为可再生能源与新型电力系统研究应用等。
    来广志(1969—), 男, 陕西人, 硕士, 教授级高级经济师, 主要研究方向为新能源发展与消纳管理等。
  • 基金资助:


Aiming at the problem of micro-cracks detection of photovoltaic modules in industrial production line, in order to reduce labor cost, improve detection efficiency and quickly adapt to the micro-cracks detection of new products with the support of a few number of samples, a micro-cracks detection algorithm of solar cells based on few shot samples with multi-loss is proposed. Firstly, in order to enrich the semantic information extracted by convolutional neural network, Transformer’s multi-head attention mechanism is introduced to alleviate the impact of the distribution difference of each batch of products on crack detection, and promote the model to focus on crack information from diversified products. Secondly, the strategy of combining multiple loss functions to constrain the model training is used to optimize feature extraction. On the basis of direct classification loss, the triplet loss is used to shorten the feature distance between cracked samples. In addition, the implicit classification loss is designed to adapt to the characteristics of type differences between the two types of cells with or without cracks, and fully learn the diversity of historical component data. This algorithm can use a small number of samples to quickly extract the features of new components and detect micro-crack defects of new products accurately. The experimental results on the actual industrial production data sets show that the recall of the algorithm can be improved by 10 percentage points compared with other baseline models. This algorithm can effectively alleviate the problem of scarce samples with hidden cracks and greatly reduce the cost of frequent data labeling and model training for each batch of new products.

Key words: solar cells, micro-cracks detection, few shot samples, deep learning, feature extraction



关键词: 光伏组件, 隐裂检测, 小样本, 深度学习, 特征提取

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