An ATR architecture for algorithm development and testing

Author
Breivik, Gøril Margrethe
Løkken, Kristin Hammarstrøm
Brattli, Alvin Andreas
Palm, Hans Christian
Haavardsholm, Trym Vegard
Date Issued
2013
Permalink
https://ffi-publikasjoner.archive.knowledgearc.net/handle/20.500.12242/456
DOI
10.1117/12.2015369
Collection
Articles
Description
Breivik, Gøril Margrethe; Løkken, Kristin Hammarstrøm; Brattli, Alvin Andreas; Palm, Hans Christian; Haavardsholm, Trym Vegard. An ATR architecture for algorithm development and testing. Proceedings of SPIE, the International Society for Optical Engineering 2013 ;Volum 8744.
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Abstract
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 exible, 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 fl ows 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.
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