Efficient ai adaption using synthetic data

被引：0
|
作者：
Blank A. [1 ]
Baier L. [1 ]
Kedilioglu O. [1 ]
Zhu X. [1 ]
Metzner M. [1 ]
Franke J. [1 ]
机构：
[1] Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Egerlandstr. 7, Erlangen
来源：
WT Werkstattstechnik | 2021年 / 111卷 / 10期
关键词：
D O I：
10.37544/1436-4980-2021-10-105
中图分类号：
学科分类号：
摘要：
Production is characterized by an antagonism between flexibility and productivity. Deep Learning-based autonomous robot skills for object manipulation offer potential to solve existing challenges. Currently, the effort to generate appropriate datasets to adapt new components is time-consuming. In this research context, we present and evaluate a method for time-efficient data generation for object recognition based on synthetic data. © 2021, VDI Fachmedien GmBH & Co. KG. All rights reserved.
引用
收藏
页码：759 / 762
页数：3
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