Ship Detection System Based On Optical Satellite Remote Sensing Data

With the continuous improvement of satellite-borne remote sensing technology,highresolution remote sensing data has increased rapidly.The massive amount of satellite-borne remote sensing data places higher requirements on satellite storage and downlink transmission capabilities.The current limited satellite data transmission bandwidth can no longer meet the transmission requirements of remote sensing data.This article is oriented to spaceborne ship inspection technology,and designs and implements a field programmable logic array(Field Programmable Gate Array,FPGA)-based optical remote sensing data ship inspection system.First,conduct a demand analysis based on the source of the project in this article.According to functional requirements,the ship inspection system is divided into a board design module,a ship inspection algorithm hardware implementation module,and a simulation source software development module.The board design module is mainly to design the hardware circuit around the main control chip to build a hardware platform for the ship inspection system.The ship detection algorithm hardware implementation module is mainly used for ship detection of remote sensing data in the embedded hardware platform,distinguishing whether the massive remote sensing data contains ship target data,eliminating invalid data that does not contain ship targets,and storing it for the later stage Equipment and transmission equipment reduce the pressure of data transmission.The simulation source software development module is mainly used to simulate the on-board operating environment and provide data input for the ship inspection system.Secondly,in view of the requirements of on-board applications,a computationally intensive,low-power FPGA is selected as the hardware platform for the operation of the ship detection algorithm.In the existing ship detection algorithms,the lightweight and fast running speed YOLOv3(You Only Look Once)deep learning network is selected as the ship detection algorithm.In view of FPGA hardware characteristics,under the condition of limited hardware resources,network lightweight design and network acceleration design are adopted to realize the algorithm migration of YOLOv3 deep learning network on FPGA hardware platform.Finally,the optical remote sensing data ship detection system designed in this paper was tested on 200 pieces of visible light remote sensing data,and it reached the experimental results of 0.33 fps detection rate,85.2% accuracy rate,14.7% false alarm rate and 6.408 W operating power consumption.Comparing the experimental results in this article with the experimental results in the Jetson TX1 development board,the single-core computing power is increased by 56 times.The major breakthrough in this paper is to transform the commonly used graphics processing unit(GPU)for artificial intelligence into an FPGA hardware platform with flexible design and high parallelism,which provides a priori design for the subsequent implementation of the ship inspection system in the localized FPGA chip.