Digging Motion Classification of Hydraulic Excavator by LSTM Using Time Series Data
Digging Motion Classification of Hydraulic Excavator by LSTM Using Time Series Data
カテゴリ: 部門大会
論文No: SS1-3
グループ名: 【C】2021年電気学会電子・情報・システム部門大会
発行日: 2021/09/08
タイトル(英語): Digging Motion Classification of Hydraulic Excavator by LSTM Using Time Series Data
著者名: Makino Yasuhiro(広島大学),Oguma Shota(広島大学),Ohno Shuichi(大阪市立大学),Iwasaki Kazuhiro(コベルコ建機(株))
著者名(英語): Yasuhiro Makino (Hiroshima University),Shota Oguma (Hiroshima University),Shuichi Ohno (Osaka City University),Kazuhiro Iwasaki (KOBELCO Construction Machinery Co.,Ltd)
キーワード: 深層学習|油圧ショベル|機械学習|掘削動作|Deep learning|Hydraulic Excavator|Machine learning|Digging motion
要約(日本語): In the construction field, it is important to automatically classify motions of a construction machine to improve productivity and safety. In recent years, the recognition technology using deep learning has been developed for image recognition. However, since a construction machine moves around in a construction field, motion classification by image recognition requires a large number of cameras. Motion classification using sensor data of a construction machine is effective. In this study, we classify the digging motion of a hydraulic excavator by Long Short-Term Memory (LSTM) and reports classification results.
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