FPA-DNN: A Forward Propagation Acceleration based Deep Neural Network for Ship Detection

Abstract

Ship detection in optical satellite images has played an important role in the field of remote sensing for a long time. Many detection methods have been proposed to address the ship detection problem, and most of them mainly focus on the improvement of detection accuracy but rarely pay attention to the detection speed. In this paper, we not only consider the improvement of detection accuracy, but also try to speed up the detection process. Based on the YOLOv2 model, we propose a forward propagation acceleration-based deep neural network model (FPA-DNN) to enhance the performance of the ship detection. The FPA-DNN model is a hybrid learning model, in which the deep neural network model LSDN can effectively reduce the number of parameters and improve the detection speed with no accuracy loss, and the pruning based forward propagation acceleration algorithm can remove the redundant convolution kernels and further speed up the detection process. Experimental results on the optical remote sensing image dataset show that, compared with several state-of-the-art deep learning models, 1) the LSDN model outperforms the others on the detection accuracy and detection speed; and 2) the FPA-DNN model can further improve the detection accuracy and speed up the detection process significantly.