TY - GEN
T1 - A Maritime Target Detector Based on CNN and Embedded Device for GF-3 Images
AU - Zhao, Chen
AU - Wang, Pengbo
AU - Wang, Jian
AU - Men, Zhirong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Recently, with the development of deep learning and the springing up of synthetic aperture radar (SAR) images, SAR maritime target detection based on convolutional neural network (CNN) has become a hot issue. However, most related work is realized on general purpose hardware like CPU or GPU, which is energy consuming, non-real-time and unable to be deployed on embedded devices. Aiming at this problem, this paper proposes a method to deploy a model of SAR maritime target detection network on an embedded device which employs custom artificial intelligence streaming architecture (CAISA). Moreover, the model is trained and tested on the Gaofen-3 (GF-3) spaceborne SAR images, which include six different kinds of maritime targets. Experiments based on the GF-3 dataset show the method is practicable and extensible.
AB - Recently, with the development of deep learning and the springing up of synthetic aperture radar (SAR) images, SAR maritime target detection based on convolutional neural network (CNN) has become a hot issue. However, most related work is realized on general purpose hardware like CPU or GPU, which is energy consuming, non-real-time and unable to be deployed on embedded devices. Aiming at this problem, this paper proposes a method to deploy a model of SAR maritime target detection network on an embedded device which employs custom artificial intelligence streaming architecture (CAISA). Moreover, the model is trained and tested on the Gaofen-3 (GF-3) spaceborne SAR images, which include six different kinds of maritime targets. Experiments based on the GF-3 dataset show the method is practicable and extensible.
KW - convolutional neural network (CNN)
KW - embedded device
KW - maritime target detection
KW - synthetic aperture radar (SAR)
UR - https://www.scopus.com/pages/publications/85083519503
U2 - 10.1109/APSAR46974.2019.9048264
DO - 10.1109/APSAR46974.2019.9048264
M3 - 会议稿件
AN - SCOPUS:85083519503
T3 - 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019
BT - 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019
Y2 - 26 November 2019 through 29 November 2019
ER -