FPGAとDeep Learningを用いたADASの構築

• 中村 太郎

  九工大

• 浅海 賢一

  九工大

• 小森 望充

  九工大

書誌事項

タイトル別名

• Construction of ADAS using FPGA and Deep Learning

抄録

Today, training and execution of deep learning is almost carried out on GPU because of extensive calculation. However, using GPU have problems of power consumption and portability. This paper presents the way to recognize and estimate size of automobile license plate using deep learning on FPGA. Binarized Neural Network to reduce calculation on PYNQ-Z1 board to design of hardware is used for the image processing. As a result, the recognition accuracy of automobile license plate was 92%, and the size estimation accuracy of automobile license plate was 87% within ± 8 pixels. In addition, processing speed of hardware was 1,604 microseconds and it had about five hundred times more than CPU.

収録刊行物

• ロボティクス・メカトロニクス講演会講演概要集

  ロボティクス・メカトロニクス講演会講演概要集 2019 (0), 2P2-I02-, 2019

  一般社団法人 日本機械学会

キーワード

• Deep Learning
• FPGA
• Binarized Neural Network
• Automobile License Plate Recognition
• ADAS

詳細情報

• CRID: 1390565134809957888
• NII論文ID: 130007775290
• DOI: 10.1299/jsmermd.2019.2p2-i02
• ISSN: 24243124
• Web Site: https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_2P2-I02/_pdf
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可