Multi-objective Cartesian Genetic Programming optimization of morphological filters in navigation systems for Visually Impaired People

Autor(es)

Batista Dourado, Antonio Miguel ^{[1, 2]} ; Pedrino, Emerson Carlos ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Rod Washington Luis, Km 235, PO 676, Sao Carlos, SP - Brazil

^[2] Fed Inst Sao Paulo, Av Zelia Lima Rosa, Boituva, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: APPLIED SOFT COMPUTING; v. 89, APR 2020.
• Citações Web of Science: 0

Resumo

Navigation systems for Visually Impaired People (VIP) have improved in the last decade, incorporating many features to ensure navigation safety. Such systems often use grayscale depth images to segment obstacles and paths according to distances. However, this approach has the common problem of unknown distances. While this can be solved with good quality morphological filters, these might be too complex and power demanding. Considering navigation systems for VIP rely on limited energy sources that have to run multiple tasks, fixing unknown distance areas without major impacts on power consumption is a definite concern. Multi-objective optimization algorithms might improve filters' energy efficiency and output quality, which can be accomplished by means of different quality vs. complexity trade-offs. This study presents NSGA2CGP, a multi-objective optimization method that employs the NSGA-II algorithm on top of Cartesian Genetic Programming to optimize morphological filters for incomplete depth images used by navigation systems for VIP. Its goal is to minimize output errors and structuring element complexity, presenting several feasible alternatives combining different levels of filter quality and complexity-both of which affect power consumption. NSGA2CGP-optimized filters were deployed into an actual embedded platform, so as to experimentally measure power consumption and execution time. We also propose two new fitness functions based on existing approaches from literature. Results showed improvements in visual quality, performance, speed and power consumption, thanks to our proposed error function, proving NSGA2CGP as a solid method for developing and evolving efficient morphological filters. (C) 2020 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 17/26421-3 - Investigação do uso de Sistemas Inteligentes para Mapeamento Eficiente de Aplicações em Arquiteturas Many-Core
• Beneficiário: Emerson Carlos Pedrino
• Modalidade de apoio: Bolsas no Exterior - Pesquisa