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

doi:10.1109/TII.2020.3008021

	Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network
	Su, Binyi 1; Chen, Haiyong 1; Chen, Peng1 ; Bian, Guibin2 ; Liu, Kun 1; Liu, Weipeng 1
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN	1551-3203
	2021-06-01
卷号	17 期号:6 页码:4084-4095
通讯作者	Chen, Haiyong(haiyong.chen@hebut.edu.cn)
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.
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
DOI	10.1109/TII.2020.3008021
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:000626556300036
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：78[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44162
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Chen, Haiyong
作者单位	1.Hebei Univ Technol, Sch Artificial Intelligence & Data Sci, Tianjin 300130, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100000, 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.