Presentation 2020-03-05
Deep Neural Networks for Object Detection and Classification on Domestic Service Robots
Yutaro Ishida, Hakaru Tamukoh,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) We propose a semi-automatic data set generation method, and a system integration method of robot operating system (ROS) and an field programmable gate array (FPGA) which implemented deep neural networks (DNN), to apply DNN to object detection and recognition on domestic service robots. The proposed methods reduce the manpower required to create a dataset, and implement DNN which is high speed processing and low power consumption into robots. We trained DNN using a dataset generated by the proposed method, implemented it on a robot, and evaluated the number of successful object grasps. In addition, we implemented DNN with the proposed method and other processors, and evaluated these processing performance.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) DNN / Dataset generation / ROS / FPGA
Paper # SIS2019-45
Date of Issue 2020-02-27 (SIS)

Conference Information
Committee SIS
Conference Date 2020/3/5(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Saitama Hall
Topics (in Japanese) (See Japanese page)
Topics (in English) Soft Computing, etc.
Chair Takayuki Nakachi(NTT)
Vice Chair Noriaki Suetake(Yamaguchi Univ.) / Tomoaki Kimura(Kanagawa Inst. of Tech.)
Secretary Noriaki Suetake(Tokyo Metropolitan Univ.) / Tomoaki Kimura(Kindai Univ.)
Assistant Hideaki Misawa(National Inst. of Tech., Ube College) / Yukihiro Bandoh(NTT)

Paper Information
Registration To Technical Committee on Smart Info-Media Systems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Deep Neural Networks for Object Detection and Classification on Domestic Service Robots
Sub Title (in English)
Keyword(1) DNN
Keyword(2) Dataset generation
Keyword(3) ROS
Keyword(4) FPGA
1st Author's Name Yutaro Ishida
1st Author's Affiliation Kyushu Institute of Technology(Kyutech)
2nd Author's Name Hakaru Tamukoh
2nd Author's Affiliation Kyushu Institute of Technology(Kyutech)
Date 2020-03-05
Paper # SIS2019-45
Volume (vol) vol.119
Number (no) SIS-458
Page pp.pp.45-50(SIS),
#Pages 6
Date of Issue 2020-02-27 (SIS)