Pattern recognition on FPGA for aerospace applications

• Autor(a) principal: Silva, Francisco de Assis Tavares Ferreira da
• Data de Publicação: 2021
• Outros Autores: Almeida Filho, Magno Prudêncio de, Lucena, Antonio Macilio Pereira de, Nowosad, Alexandre Guirland
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/19181
• topic: Satélites inteligentes; Inteligência artificial em hardware; Visão computacional; Inteligência artificial em tempo real; Aprendizagem de máquina; Nanosatélites; Morfologia matemática; Aplicações aeroespaciais; Reconhecimento de padrões; Sensoriamento remoto.; Intelligent satellites; Nanosatellites; Artificial intelligence in hardware; Computer vision; Machine learning; Mathematical morphology; Pattern recognition; Real time systems; Aerospace applications; Remote sensing.; Satélites inteligentes; Nanosatélites; Inteligencia artificial en hardware; Visión artificial; Aprendizaje automática; Morfología matemática; Reconocimiento de patrones; Inteligencia artificial en tiempo real; Aplicaciones aeroespaciales

This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors.