基于深度学习的海上目标跟踪与预警系统设计与实现

Design and implementation of maritime target tracking and early warning system based on deep learning

在传统的方法中,通常需要使用大量的计算资源来进行图像处理和特征提取,而这种方法往往无法满足实时性和高精度的要求。针对海上目标跟踪和预警问题,文章提出一种基于深度学习的方法,并基于此方法建立了基于深度学习的目标跟踪和预警系统。该系统采用卷积神经网络作为基础模型,通过对海面上的目标进行识别和分类,实现了对目标的位置和运动状态的准确预测。

This study aims to propose a deep learning-based approach for maritime target tracking and early warning.In traditional methods,it is usually necessary to use a lot of computing resources for image processing and feature extraction,but this method often can not meet the requirements of real-time and high precision.Therefore,this paper proposes a new approach-target tracking and early warning system based on deep learning.The system uses convolutional neural network(CNN)as the basic model,and realizes the accurate prediction of the position and motion state of the target by recognizing and classifying the target on the sea surface.