skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices

Weighted boxes fusion: Ensembling boxes from different object detection models

Solovyev, Roman ; Wang, Weimin ; Gabruseva, Tatiana

Image and vision computing, 2021-03, Vol.107, p.104117, Article 104117 [Periódico revisado por pares]

Elsevier B.V

Texto completo disponível

Citações Citado por
  • Título:
    Weighted boxes fusion: Ensembling boxes from different object detection models
  • Autor: Solovyev, Roman ; Wang, Weimin ; Gabruseva, Tatiana
  • Assuntos: Computer vision ; Deep learning ; Object detection
  • É parte de: Image and vision computing, 2021-03, Vol.107, p.104117, Article 104117
  • Descrição: Object detection is a crucial task in computer vision systems with a wide range of applications in autonomous driving, medical imaging, retail, security, face recognition, robotics, and others. Nowadays, neural networks-based models are used to localize and classify instances of objects of particular classes. When real-time inference is not required, ensembles of models help to achieve better results. In this work, we present a novel method for fusing predictions from different object detection models: weighted boxes fusion. Our algorithm utilizes confidence scores of all proposed bounding boxes to construct averaged boxes. We tested the method on several datasets and evaluated it in the context of Open Images and COCO Object Detection challenges, achieving top results in these challenges. The 3D version of boxes fusion was successfully applied by the winning teams of Waymo Open Dataset and Lyft 3D Object Detection for Autonomous Vehicles challenges. The source code is publicly available at GitHub (Solovyev, 2019 [31]). We present a novel method for combining predictions in ensembles of different object detection models: weighted boxes fusion. This method significantly improves the quality of the fused predicted rectangles for an ensemble. We tested the method on several datasets and evaluated it in the context of the Open Images and COCO Object Detection challenges. It helped to achieve top results in these challenges. The source code is publicly available at GitHub. •Novel method was proposed for combining predictions in ensembles of different object detection models.•Method significantly improves the quality of the fused predicted rectangles for an ensemble. The code is available at GitHub.•Method was tested on several datasets and evaluated in the context of the Open Images and COCO Object Detection challenges.
  • Editor: Elsevier B.V
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.