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

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.