多损失融合的小样本光伏组件隐裂检测算法

doi:10.3778/j.issn.1673-9418.2111036

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (2): 458-467.DOI: 10.3778/j.issn.1673-9418.2111036

多损失融合的小样本光伏组件隐裂检测算法

那峙雄¹^,⁺(), 樊涛², 孙涛¹, 谢祥颖³^,¹, 来广志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

收稿日期:2021-11-05 修回日期:2022-01-12 出版日期:2022-02-01 发布日期:2022-01-17
通讯作者: + E-mail: hellonazx@163.com
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。
樊涛（1971—）,男,山东人,硕士,教授级高级工程师,主要研究方向为企业信息化、电力系统自动化、能源互联网等。
孙涛（1968—）,男,新疆人,高级工程师,主要研究方向为电力系统自动化、能源工业互联网等。
谢祥颖（1979—）,男,湖北人,博士研究生,高级工程师,主要研究方向为工业互联网。
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:
国家重点研发计划(2018YFB1500800);国家电网有限公司科技项目(SGTJDK00DYJS2000148)

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. 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)

摘要/Abstract

摘要：

针对工业生产线光伏组件隐性纹检测问题,为了降低人力成本,提高检测效率,并快速适应新型产品的隐裂检测,提出了一种多损失融合的小样本光伏组件隐裂检测算法。首先,为丰富卷积神经网络提取的语义信息,引入了Transformer的多头注意力机制,缓解各批次产品的分布差异对隐裂检测的影响,促使模型从多样化产品中关注于隐裂信息;其次,利用多损失结合约束模型训练的策略优化特征提取,在直接分类损失的基础上,利用三元组损失拉近含隐裂样本间特征距离;此外,设计了隐式分类损失以适应有无隐裂两类电池片内部也存在类型差异的特点,充分学习历史组件数据的多样性。该算法能够快速提取新型组件特征,利用少量的样本特征对新产品隐裂缺陷进行准确检测。在实际工业生产数据集上的实验结果表明,该算法对新型组件的隐裂检测的召回率相较于其他基线模型可提高10个百分点,能够有效缓解含隐裂样本数量不足的问题,极大地降低了频繁对每批新产品进行数据标记和训练的开销。

关键词: 光伏组件, 隐裂检测, 小样本, 深度学习, 特征提取

Abstract:

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

中图分类号:

TP391

那峙雄, 樊涛, 孙涛, 谢祥颖, 来广志. 多损失融合的小样本光伏组件隐裂检测算法[J]. 计算机科学与探索, 2022, 16(2): 458-467.

NA Zhixiong, FAN Tao, SUN Tao, XIE Xiangying, LAI Guangzhi. Micro-cracks Detection of Solar Cells Based on Few Shot Samples with Multi-loss[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(2): 458-467.

图/表 13

图1 有隐裂电池片

Fig.1 Cracked cells

图2 隐裂检测算法流程图

Fig.2 Workflow of micro-cracks detection algorithm

图3 残差结构示意图

Fig.3 Diagram of residual structure

图4 注意力计算模块示意图

Fig.4 Structure of Transformer block

图5 特征图序列化

Fig.5 Feature graph serialization

图6 训练损失收敛曲线

Fig.6 Loss convergence curve of training

表1 K-shot下检测结果

Table 1 Detection results under K-shot

K-shot	召回率/%	精确率/%	F1-score
5	85.07	75.09	0.797 69
10	85.13	78.44	0.816 48
15	85.00	81.25	0.830 83

图7 仅基于少量样本调整模型时的检测效果

Fig.7 Detection effect based on small sample adjustment

表2 5-shot下消融实验结果

Table 2 Results of ablation experiment under 5-shot

网络结构	损失策略	召回率/%	精确率/%	$F 1 - score$
VGG19	交叉熵损失	65.87	68.46	0.671 40
	+三元组损失	71.47	69.55	0.704 97
	+三元组&隐式	71.93	71.52	0.717 24
ResNet50	交叉熵损失	65.53	64.38	0.649 50
	+三元组损失	75.07	70.20	0.725 53
	+三元组&隐式	78.33	75.55	0.769 15
ResNet50+ SE Block	交叉熵损失	68.00	65.56	0.667 58
	+三元组损失	79.13	71.49	0.751 16
	+三元组&隐式	83.27	72.26	0.773 75
ResNet50+ Non-local Block	交叉熵损失	66.60	65.76	0.661 77
	+三元组损失	80.07	72.29	0.759 81
	+三元组&隐式	84.47	73.62	0.786 73
ResNet50+ Transformer Block	交叉熵损失	69.67	63.22	0.662 88
	+三元组损失	76.47	74.25	0.753 44
	+三元组&隐式	85.07	75.09	0.797 69

表2 5-shot下消融实验结果

Table 2 Results of ablation experiment under 5-shot

网络结构	损失策略	召回率/%	精确率/%	$F 1 - score$
VGG19	交叉熵损失	65.87	68.46	0.671 40
	+三元组损失	71.47	69.55	0.704 97
	+三元组&隐式	71.93	71.52	0.717 24
ResNet50	交叉熵损失	65.53	64.38	0.649 50
	+三元组损失	75.07	70.20	0.725 53
	+三元组&隐式	78.33	75.55	0.769 15
ResNet50+ SE Block	交叉熵损失	68.00	65.56	0.667 58
	+三元组损失	79.13	71.49	0.751 16
	+三元组&隐式	83.27	72.26	0.773 75
ResNet50+ Non-local Block	交叉熵损失	66.60	65.76	0.661 77
	+三元组损失	80.07	72.29	0.759 81
	+三元组&隐式	84.47	73.62	0.786 73
ResNet50+ Transformer Block	交叉熵损失	69.67	63.22	0.662 88
	+三元组损失	76.47	74.25	0.753 44
	+三元组&隐式	85.07	75.09	0.797 69

图8 有隐裂电池片激活图

Fig.8 Activation maps on cracked cells

图9 无隐裂电池片激活图

Fig.9 Activation maps on normal cells

表3 模型属性对比结果

Table 3 Comparison results of model attributes

网络结构	参数量/10⁶	计算量/ GFLOPs	检测速度/（张/s）
VGG19	20.0	19.5	93
ResNet50	23.5	4.1	268
ResNet50+SE	25.1	4.1	266
ResNet50+Non-local	48.7	5.3	226
ResNet50+Transformer	28.3	4.9	233

表4 5-shot 下对比实验结果

Table 4 Comparison results under 5-shot

对比方法	召回率/%	精确率/%	F1-score
ProtoNet^[14]	68.73	70.02	0.693 69
RelationNet^[15]	71.26	53.62	0.611 94
MAML^[18]	67.07	69.01	0.680 26
OptNet^[19]	64.86	60.24	0.624 65
多损失融合	85.07	75.09	0.797 69

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多损失融合的小样本光伏组件隐裂检测算法

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

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