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| Paper Abstract and Keywords | ||
| Presentation | 2022-06-10 13:00 [Tutorial Lecture] How to build a High-Precision and Efficient Robot Vision: Dataset Generation and Hardware Implementation for Deep Learning Hakaru Tamukoh (Kyutech)  SIS2022-10 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This tutorial lecture explains a construction method for high-precision and efficient robot vision that includes a semi-automatic dataset generation method and an implementation method of deep neural networks (DNNs) on field-programmable gate arrays (FPGAs). The proposed dataset generation method ultimately reduces the time-consuming manual annotation process, and a generated dataset for DNNs can be prepared in about two hours. I show the evaluation results for a DNN trained by the generated dataset under real-world conditions through robot competitions such as RoboCup and World Robot Summit. We also show an example of FPGA implementation of YOLO v3 tiny. Through this presentation, I show examples of solutions for dataset preparation and power-consumption issues, which are significant barriers to edge applications of artificial intelligence. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep Learning / DNN / Dataset / FPGA / Robot Vision / RoboCup / World Robot Summit / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 62, SIS2022-10, pp. 45-48, June 2022. | |
| Paper # | SIS2022-10 | |
| Date of Issue | 2022-06-02 (SIS) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SIS2022-10 |
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| Conference Information | |
| Committee | SIS IPSJ-AVM |
| Conference Date | 2022-06-09 - 2022-06-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | KIT(Wakamatsu Campus) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Intelligent Multimedia Systems, Applied Embedded Systems, Three-Dimensional Image Technology (3DIT), etc. |
| Paper Information | |
| Registration To | SIS |
| Conference Code | 2022-06-SIS-AVM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | How to build a High-Precision and Efficient Robot Vision: Dataset Generation and Hardware Implementation for Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | DNN |
| Keyword(3) | Dataset |
| Keyword(4) | FPGA |
| Keyword(5) | Robot Vision |
| Keyword(6) | RoboCup |
| Keyword(7) | World Robot Summit |
| Keyword(8) | |
| 1st Author's Name | Hakaru Tamukoh |
| 1st Author's Affiliation | Kyushu Institute of Technology (Kyutech) |
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| Speaker | Author-1 |
| Date Time | 2022-06-10 13:00:00 |
| Presentation Time | 40 minutes |
| Registration for | SIS |
| Paper # | SIS2022-10 |
| Volume (vol) | vol.122 |
| Number (no) | no.62 |
| Page | pp.45-48 |
| #Pages | 4 |
| Date of Issue | 2022-06-02 (SIS) |
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