| Presentation | 2018-12-07 Hardware Oriented Object Recognition Neural Network using Depth Image Yuma Yoshimoto, Hakaru Tamukoh, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In recent years, deep learning using Convolutional Neural Network (CNN) has attracted attention as a powerful method for general object recognition. General object recognition is a task for recognizing objects from pictures of a real-world as a general name. General object recognition algorithm is required for service robots or embedded systems. In these cases, real-time processing is required, however, real-time processing by software is difficult. Therefore, high-speed operation by hardware is required. A Field Programmable Gate Arrays (FPGA) is a high-performance device with low-power consumption. To implement an algorithm into FPGAs, a hardware-oriented algorithm is required to reduce hardware resource utilization, because the resources of FPGA are limited. In this paper, we propose a dq{Binarized Dual Stream VGG-16 (BDS-VGG16)} which is one of the binarized convolutional neural networks using Binarized Neural Network (BNN) as a hardware-oriented algorithm for FPGA implementation. The proposed network is based on a Dual Stream VGG-16 (DS-VGG16) which realizes high accuracy by using RGB images and Depth images. The BDS-VGG16 is a combination of the DS-VGG16 and a Binarized Neural Network (BNN). In the experimental results show that the proposed algorithm is high accuracy. Also, the proposed method is efficient for FPGA implementation because the BDS-VGG16 completely remove the multipliers from the ordinary algorithm: therefore, the proposed algorithm can be implemented without multipliers. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Generic Object Recognition / FPGA / CNN / Depth Images |
| Paper # | SIS2018-32 |
| Date of Issue | 2018-11-29 (SIS) |
| Conference Information | |
| Committee | SIS |
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| Conference Date | 2018/12/6(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hagi Civic Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Smart Personal Systems, etc. |
| Chair | Takayuki Nakachi(NTT) |
| Vice Chair | Noriaki Suetake(Yamaguchi Univ.) / Tomoaki Kimura(Kanagawa Inst. of Tech.) |
| Secretary | Noriaki Suetake(Kyushu Inst. of Tech.) / Tomoaki Kimura(Tokyo Metropolitan Univ.) |
| Assistant | Takanori Koga(National Inst. of Tech. Tokuyama College) / Hideaki Misawa(National Inst. of Tech., Ube College) |
| Paper Information | |
| Registration To | Technical Committee on Smart Info-Media Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hardware Oriented Object Recognition Neural Network using Depth Image |
| Sub Title (in English) | |
| Keyword(1) | Generic Object Recognition |
| Keyword(2) | FPGA |
| Keyword(3) | CNN |
| Keyword(4) | Depth Images |
| 1st Author's Name | Yuma Yoshimoto |
| 1st Author's Affiliation | Kyushu Institute of Technology(KIT) |
| 2nd Author's Name | Hakaru Tamukoh |
| 2nd Author's Affiliation | Kyushu Institute of Technology(KIT) |
| Date | 2018-12-07 |
| Paper # | SIS2018-32 |
| Volume (vol) | vol.118 |
| Number (no) | SIS-346 |
| Page | pp.pp.55-60(SIS), |
| #Pages | 6 |
| Date of Issue | 2018-11-29 (SIS) |