CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
Defect Detection for Mobile Phone Cover Glass using Deep Neural Networks
Zhang ZT(张正涛); Zhang ZT(张正涛)
Source PublicationJounral of Precision Engineering and Manufacturing
2018
Volume19Issue:6Pages:801-810
Abstract
The emergency of surface defect would significantly influence the quality of MPCG
(Mobile Phone Cover Glass). Therefore, efficient defect detection is highly required in
the manufacturing process. Focusing on the problem, an automatic detection system is
developed in this paper. The system adopts backlight imaging technology to improve
the signal to noise ration and imaging effect. Then, a modified segmentation method is
presented for defect extraction and measurement based on deep neural networks. In
the method, a novel data generation process is provided, with which the drawback that
huge amount of data is required for training deep structured networks can be
overcome. Finally, experiments are well conducted to verify that satisfactory
performance is achieved with the proposed method.
KeywordMobile Phone Cover Glass Defect Inspection Deep Learning Sementic Segmentation
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21666
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorZhang ZT(张正涛)
Recommended Citation
GB/T 7714
Zhang ZT,Zhang ZT. Defect Detection for Mobile Phone Cover Glass using Deep Neural Networks[J]. Jounral of Precision Engineering and Manufacturing,2018,19(6):801-810.
APA Zhang ZT,&张正涛.(2018).Defect Detection for Mobile Phone Cover Glass using Deep Neural Networks.Jounral of Precision Engineering and Manufacturing,19(6),801-810.
MLA Zhang ZT,et al."Defect Detection for Mobile Phone Cover Glass using Deep Neural Networks".Jounral of Precision Engineering and Manufacturing 19.6(2018):801-810.
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