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AMARO - Autonomous real-time detection of moving maritime objects Introducing a flight experiment for an on-board ship detection system

机译:Amaro - 移动海上物体的自主实时检测,介绍车载船舶检测系统的飞行实验

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Motivated by politics and economy, the monitoring of the world wide ship traffic is a field of high topicality. To detect illegal activities like piracy, illegal fishery, ocean dumping and refugee transportation is of great value. The analysis of satellite images on the ground delivers a great contribution to situation awareness. However, for many applications the up-to-dateness of the data is crucial. With ground based processing, the time between image acquisition and delivery of the data to the end user is in the range of several hours. The highest influence to the duration of ground based processing is the delay caused by the transmission of the large amount of image data from the satellite to the processing centre on the ground. One expensive solution to this issue is the usage of data relay satellites systems like EDRS. Another approach is to analyse the image data directly on-board of the satellite. Since the product data (e.g. ship position, heading, velocity, characteristics) is very small compared to the input image data, real-time connections provided by satellite telecommunication services like Iridium or Orbcomm can be used to send small packets of information directly to the end user without significant delay. The AMARO (Autonomous real-time detection of moving maritime objects) project at DLR is a feasibility study of an on-board ship detection system involving a real-time low bandwidth communication. The operation of a prototype on-board ship detection system will be demonstrated on an airborne platform. In this article, the scope, aim and design of a flight experiment for an on-board ship detection system scheduled for mid of 2018 is presented. First, the scope and the constraints of the experiment are explained in detail. The main goal is to demonstrate the operability of an automatic ship detection system on board of an airplane. For data acquisition the optical high resolution DLR MACS-MARE camera (VIS/NIR) is used. The system will be able to send product data, like position, size and a small image of the ship directly to the user's smart-phone by email. The time between the acquisition of the image data and the delivery of the product data to the end-user is aimed to be less than three minutes. For communication, the SMS-like Iridium Short Burst Data (SBD) Service was chosen, providing a message size of around 300 Bytes. Under optimal sending/receiving conditions, messages can be transmitted bidirectional every 20 seconds. Due to the very small data bandwidth, not all product data may be transmittable at once, for instance, when flying over busy ships traffic zones. Therefore the system offers two services: a query and a push service. With the query service the end user can explicitly request data of a defined location and fixed time period by posting queries in an SQL-like language. With the push service, events can be predefined and messages are received automatically, if and when the event occurs. Finally, the hardware set-up, details of the ship detection algorithms and the current status of the experiment is presented.
机译:受政治和经济的动机,对全球船舶交通的监测是一个高题址的领域。要检测盗版等非法活动,非法渔业,海洋倾销和难民运输具有很大的价值。地面上的卫星图像分析为情境意识提供了巨大贡献。但是,对于许多应用程序,数据的最新性至关重要。利用基于地面的处理,图像获取和向最终用户的数据交付之间的时间在几个小时的范围内。对基于地面处理的持续时间的最高影响是由从卫星从卫星传输到地面上的加工中心的大量图像数据引起的延迟。对此问题的一个昂贵的解决方案是使用EDRS等数据中继卫星系统的使用。另一种方法是直接分析卫星板上的图像数据。由于与输入图像数据相比,产品数据(例如船舶位置,标题,速度,特性)非常小,因此卫星电信服务等实时连接,如铱星或ORBCOMM,可用于直接向信息发送小数据包最终用户无明显延迟。 DLR的Amaro(自主实时检测移动海上物体)项目是一个涉及实时低带宽通信的板载船舶检测系统的可行性研究。在机载平台上将对原型车载船舶检测系统进行操作。在本文中,介绍了2018年中期的船上船舶检测系统飞行试验的范围,瞄准和设计。首先,详细解释实验的范围和约束。主要目标是展示飞机船上自动船舶检测系统的可操作性。对于数据采集,使用光学高分辨率DLR MACS-MARE(VI / NIR)。该系统将能够通过电子邮件直接向用户的智能手机直接发送产品数据,如船舶的位置,大小和小型图像。图像数据的获取和向最终用户的产品数据交付之间的时间旨在小于三分钟。对于通信,选择SMS样铱短突发数据(SBD)服务,提供约300字节的消息大小。在最佳发送/接收条件下,可以每20秒传输消息一次。由于数据带宽非常小,而不是所有产品数据可以一次可传输,例如,在繁忙的船舶交通区飞行时可以可传输。因此,系统提供了两个服务:查询和推送服务。对于查询服务,最终用户可以通过在类似SQL的语言中发布查询来显式请求定义的位置和固定时间段的数据。通过推送服务,可以预定义事件,如果发生事件时,可以自动接收消息。最后,介绍了船舶检测算法的硬件设置,细节和实验的当前状态。

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