Avaliação de Técnicas de Detecção de Pedestres para Veículos Autônomos

  • Gabriel Reis UFPA
  • Wellington Lobato UNICAMP
  • Denis Rosário UFPA
  • Eduardo Cerqueira UFPA
  • Leandro A. Villas UNICAMP

Resumo


A detecção de objetos é uma das principais aplicações dentro do contexto dos Veículos Autônomos (AVs). As aplicações de detecção de pedestres compõem a camada de percepção veicular, utilizando sensores e câmeras para detectar a presença de objetos na área próxima ao AV. No entanto, as técnicas de detecção de pedestre apresentam limitações e restrições de acordo com o comportamento do cenário veicular, principalmente devido à variação das condições de iluminação e o tamanho dos pedestres. Nesse contexto, este artigo apresenta um estudo comparativo das principais técnicas de detecção de pedestre para AVs. A avaliação considera quatro tipos de técnicas de detecção, sendo eles: Faster R-CNN, SSD, YOLO e RetinaNet. Os resultados da avaliação indicam que as abordagens YOLO e Faster R-CNN obtiveram desempenho superior em termos de tempo de processamento e detecção de pedestres, apresentando uma média de detecção de 0,58 segundos por imagem.

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Publicado
06/08/2023
REIS, Gabriel; LOBATO, Wellington; ROSÁRIO, Denis; CERQUEIRA, Eduardo; VILLAS, Leandro A.. Avaliação de Técnicas de Detecção de Pedestres para Veículos Autônomos. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 22. , 2023, João Pessoa/PB. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 61-72. ISSN 2595-6167. DOI: https://doi.org/10.5753/wperformance.2023.230699.