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An ATR architecture for algorithm development and testing

机译：用于算法开发和测试的ATR架构

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A research platform with four cameras in the infrared and visible spectral domains is under development at the Norwegian Defence Research Establishment (FFI). The platform will be mounted on a high-speed jet aircraft and will primarily be used for image acquisition and for development and test of automatic target recognition (ATR) algorithms. The sensors on board produce large amounts of data, the algorithms can be computationally intensive and the data processing is complex. This puts great demands on the system architecture; it has to run in real-time and at the same time be suitable for algorithm development. In this paper we present an architecture for ATR systems that is designed to be flexible, generic and efficient. The architecture is module based so that certain parts, e.g. specific ATR algorithms, can be exchanged without affecting the rest of the system. The modules are generic and can be used in various ATR system configurations. A software framework in C++ that handles large data flows in non-linear pipelines is used for implementation. The framework exploits several levels of parallelism and lets the hardware processing capacity be fully utilised. The ATR system is under development and has reached a first level that can be used for segmentation algorithm development and testing. The implemented system consists of several modules, and although their content is still limited, the segmentation module includes two different segmentation algorithms that can be easily exchanged. We demonstrate the system by applying the two segmentation algorithms to infrared images from sea trial recordings.
机译：挪威国防研究机构（FFI）正在开发一个在红外和可见光谱范围内具有四个摄像头的研究平台。该平台将安装在高速喷气飞机上，主要用于图像采集以及自动目标识别（ATR）算法的开发和测试。板载传感器产生大量数据，算法可能需要大量计算，并且数据处理非常复杂。这对系统架构提出了很高的要求。它必须实时运行，同时适合算法开发。在本文中，我们提出了ATR系统的体系结构，该体系结构设计得灵活，通用且高效。该架构是基于模块的，因此某些部分，例如组件。可以交换特定的ATR算法，而不会影响系统的其余部分。这些模块是通用模块，可以在各种ATR系统配置中使用。使用C ++中的软件框架来处理非线性管道中的大量数据流。该框架利用了几个并行级别，并充分利用了硬件处理能力。 ATR系统正在开发中，已达到可用于细分算法开发和测试的第一级。所实现的系统由几个模块组成，尽管它们的内容仍然有限，但是分段模块包括两个可以轻松交换的不同分段算法。我们通过将两种分割算法应用于来自海试记录的红外图像来演示该系统。

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