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Modeling of Digital Twin Workshop Based on Perception Data Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Robotized Data Collection Approach for Convolutional Neural Networks

Authors : Yiming Liu, Shaohua Zhang, Xiaohui Xiao, Miao Li

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

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Abstract

Convolutional Neural Networks are powerful tools in object classification which are widely used in Robot Vision. One of the basic requirements of this approach is the demand for a massive data set. However, in many scenarios, it is either economically expensive or difficult (impossible) to collect many valid data with few samples. To this end, in this paper we proposes an automatic approach to collecting data for food industry. First, a robotized data collection system is introduced which uses an industry robot with 6 Degree of Freedoms (DOF). Second, we analysis the key parameters of the proposed system in order to improve the quality of the training model. Finally, the effectiveness of our approach is demonstrated on real experimental platform.

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previous chapter Graspable Object Classification with Multi-loss Hierarchical Representations
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Metadata
Title
A Robotized Data Collection Approach for Convolutional Neural Networks
Authors
Yiming Liu
Shaohua Zhang
Xiaohui Xiao
Miao Li
Copyright Year
2017
Book
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65297-9

Electronic ISBN: 978-3-319-65298-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65298-6_43

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