Blurred workpiece angle detection method based on generative adversarial networks

被引：0

作者：

Hu H. ^{[1
,2
]}

Zhuang Z. ^{[1
,2
]}

Yu J. ^{[1
,2
]}

Li Z. ^{[1
,2
]}

Chen J. ^{[1
,2
]}

Hu H. ^{[1
,2
]}

机构：

[1] Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou Dianzi University, Hangzhou

[2] College of Computer and Technology, Hangzhou Dianzi University, Hangzhou

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Angle detection; Generating adversarial networks; Image deblurring; Line detection; Machine vision; Workpiece image;

D O I：

10.13196/j.cims.2019.08.008

中图分类号：

学科分类号：

摘要：

To improve the angle detection accuracy of blurred workpiece image in the complex industrial production environment for performing the effective deblurring operations on the workpiece images, a deblurring method based on Generative Adversarial Networks was proposed, which minimized the distance between deblurred image and sharp image by zero-sum game training between generative network and discriminative network. To avoid problems such as line error detection or breakage, an improved line segment detector algorithm based on line segment detector was proposed. Comparing with the multi-scale convolutional neural network deblurring method, it could be found that the proposed method had improved the detection accuracy by about 13% through experiments and data analysis. © 2019, Editorial Department of CIMS. All right reserved.

引用

页码：1936 / 1945

页数：9

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