Código-fonte e dados experimentais para redução do desbalanceamento de classes na detecção de EPI por meio de um pipeline híbrido de aumento de dados

Version 1.0
Oliveira, Mateus Norberto de; Fraidenraich, Gustavo, 2026, "Código-fonte e dados experimentais para redução do desbalanceamento de classes na detecção de EPI por meio de um pipeline híbrido de aumento de dados", https://doi.org/10.25824/redu/5KWYRS, Repositório de Dados de Pesquisa da Unicamp , V1
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Description

Este repositório contém os materiais utilizados no estudo de um pipeline híbrido de aumento de dados para a redução do desbalanceamento de classes em conjuntos de dados destinados à detecção de Equipamentos de Proteção Individual (EPI). O pipeline híbrido combina técnicas de aumento de dados online e offline, sendo o aumento de dados offline aplicado de forma direcionada às imagens que contêm instâncias de classes minoritárias.

Os materiais estão organizados nas seguintes categorias:

Código-fonte

  • Código-fonte para a aplicação do aumento de dados offline direcionado às imagens que contêm instâncias de classes minoritárias;
  • Código-fonte para a preparação dos conjuntos de dados e para a execução dos procedimentos de validação cruzada;
  • Código-fonte para o treinamento dos modelos YOLOv11, YOLOv12, YOLOv13 e YOLO26;
  • Arquivos YAML contendo configurações da arquitetura YOLOv11 com os backbones Swin Transformer e ConvNeXt.

Dados experimentais

  • Conjuntos de dados utilizados nos experimentos;
  • Conjuntos de dados contendo as imagens após a aplicação do aumento de dados offline;
  • Arquivos de texto contendo as divisões dos conjuntos de dados utilizadas nas etapas de treinamento e validação da validação cruzada.

Nota: Os materiais utilizados neste estudo não podem ser disponibilizados no momento, uma vez que os artigos relacionados ainda não foram publicados. Após a publicação dos trabalhos, os materiais serão disponibilizados neste repositório. Para mais informações, entre em contato com os responsáveis pelo projeto.

Subject Computer and Information Science
Keyword Aumento de dados, Desbalanceamento entre classes, Visão computacional, Visão por computador, Processamento de imagens, Equipamento de proteção individual, Aprendizado de máquina
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