A Maritime Target Detector Based on CNN and Embedded Device for GF-3 Images

Abstract

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.