Semantic Segmentation for People Detection on Beach Images

  • Leonardo de A. Monte UFRPE
  • Emília G. Oliveira UFRPE
  • Filipe R. Cordeiro UFRPE
  • Valmir Macario UFRPE


Nosso trabalho compara um conjunto de redes de segmentação semântica aplicados na detecção de pessoas em imagens de praia, como parte de um sistema de rastreamento automático para evitar que banhistas ultrapassem a região segura do mar. Em nossa análise, comparamos as redes de segmentação U-net, X-net, Linknet, e Unet++ usando os backbones prétreinados VGG-16 e VGG-19. Nós propomos nossa própria base de imagens, composta de 300 imagens. Os modelos foram avaliados utilizando a métrica F-score. Nossos resultados mostraram que a Linknet obteve o melhor valor de F-score, com 90.89%, enquanto a Linknet foi mais rápida que as outras redes, sem diferença estatística significativa.


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MONTE, Leonardo de A.; OLIVEIRA, Emília G.; CORDEIRO, Filipe R.; MACARIO, Valmir. Semantic Segmentation for People Detection on Beach Images. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 691-702. ISSN 2763-9061. DOI:

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