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Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network

文献类型：期刊论文

作者Su, Binyi2; Chen, Haiyong2; Chen, Peng2; Bian, Guibin1; Liu, Kun2; Liu, Weipeng2
刊名IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
出版日期2021-06-01
卷号17期号:6页码:4084-4095
关键词Photovoltaic cells Feature extraction Proposals Task analysis Shape Convolution Visualization Attention network automatic defects detection near-infrared image region proposal network (RPN) solar cell
ISSN号1551-3203
DOI10.1109/TII.2020.3008021
通讯作者Chen, Haiyong(haiyong.chen@hebut.edu.cn)
英文摘要The automatic defects detection for solar cell electroluminescence (EL) images is a challenging task, due to the similarity of defect features and complex background features. To address this problem, in this article a novel complementary attention network (CAN) is designed by connecting the novel channel-wise attention subnetwork with spatial attention subnetwork sequentially, which adaptively suppresses the background noise features and highlights the defect features simultaneously by employing the complementary advantage of the channel features and spatial position features. In CAN, the novel channel-wise attention subnetwork applies convolution operation to integrate the concatenated and discriminative output features extracted by global average pooling layer and global max pooling layer, which can make fully use of these informative features. Furthermore, a region proposal attention network (RPAN) is proposed by embedding CAN into region proposal network in faster R-CNN (convolution neutral network) to extract more refined defective region proposals, which is used to construct a novel end-to-end faster RPAN-CNN framework for detecting defects in raw EL image. Finally, some experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method performs much better than other methods in terms of defects classification and detection results in raw solar cell EL images.
资助项目National Natural Science Foundation of China[61873315] ; Natural Science Foundation of Hebei Province[F2018202078] ; Natural Science Foundation of Hebei Province[F2019202305] ; Natural Science Foundation of Hebei Province[TII-20-1900]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
WOS记录号WOS:000626556300036
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province
源URL[http://ir.ia.ac.cn/handle/173211/44161]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Chen, Haiyong
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100000, Peoples R China
2.Hebei Univ Technol, Sch Artificial Intelligence & Data Sci, Tianjin 300130, Peoples R China
推荐引用方式
GB/T 7714		 Su, Binyi,Chen, Haiyong,Chen, Peng,et al. Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2021,17(6):4084-4095.
APA Su, Binyi,Chen, Haiyong,Chen, Peng,Bian, Guibin,Liu, Kun,&Liu, Weipeng.(2021).Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,17(6),4084-4095.
MLA Su, Binyi,et al."Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 17.6(2021):4084-4095.

入库方式： OAI收割

来源：自动化研究所

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