{"id":2483,"date":"2021-03-29T03:02:40","date_gmt":"2021-03-28T16:02:40","guid":{"rendered":"https:\/\/wordpress.temp.irap-dev.org\/using-convolutional-neural-networks-to-improve-road-safety-and-save-lives\/"},"modified":"2026-04-30T12:38:05","modified_gmt":"2026-04-30T02:38:05","slug":"using-convolutional-neural-networks-to-improve-road-safety-and-save-lives","status":"publish","type":"post","link":"https:\/\/irap.org\/pt\/using-convolutional-neural-networks-to-improve-road-safety-and-save-lives\/","title":{"rendered":"Utilizando Redes Neurais Convolucionais para Melhorar a Seguran\u00e7a Rodovi\u00e1ria e Salvar Vidas"},"content":{"rendered":"

Lois Anne Leal e Chaitree Sham Baradkar participaram do projeto inovador. Desafio de 8 semanas iRAP Omdena<\/a> No in\u00edcio deste ano, eles se reuniram para encontrar solu\u00e7\u00f5es baseadas em IA para mapear o risco de acidentes e gerar dados de atributos de classifica\u00e7\u00e3o por estrelas de 1 a 5. Eles foram apenas 2 dos 50 engenheiros de IA e aprendizado de m\u00e1quina recrutados de todo o mundo, focados em encontrar uma solu\u00e7\u00e3o para a coleta e codifica\u00e7\u00e3o acelerada e inteligente de dados de atributos de estradas (AiRAP<\/a>) para salvar vidas.<\/p>\n

Lois \u00e9 Especialista em Pesquisa Cient\u00edfica, Engenheira de Aprendizado de M\u00e1quina e Desenvolvedora Certificada em TensorFlow, das Filipinas, e Chaitree \u00e9 Cientista de Dados na PharmaACE e Engenheira de Aprendizado de M\u00e1quina na Omdena, de Pune, \u00cdndia.<\/p>\n

O Desafio iRAP Omdena teve 3 objetivos principais:<\/p>\n

    \n
  1. Obter dados de acidentes georreferenciados e produzir mapas de risco iRAP com o hist\u00f3rico de acidentes por quil\u00f4metro e acidentes por quil\u00f4metro percorrido para cada usu\u00e1rio da via.<\/li>\n
  2. Obter dados sobre atributos de estradas, fluxo de tr\u00e1fego e velocidade de acordo com o padr\u00e3o global iRAP e mapear o desempenho de seguran\u00e7a e a classifica\u00e7\u00e3o por estrelas de mais de 100 milh\u00f5es de km de estradas em todo o mundo.<\/li>\n
  3. Produzir indicadores-chave de desempenho (KPIs) repet\u00edveis para infraestrutura rodovi\u00e1ria que possam servir de base para o monitoramento anual do desempenho.<\/li>\n<\/ol>\n

    Lois e Chaitree contribu\u00edram para o segundo objetivo ao obterem automaticamente o componente crucial da contagem de ve\u00edculos no atributo de tr\u00e1fego ou fluxo de ve\u00edculos, utilizando imagens de sat\u00e9lite com o aux\u00edlio de Intelig\u00eancia Artificial.<\/p>\n

    Leia o blog que eles publicaram: \u201cUtilizando Redes Neurais Convolucionais para Melhorar a Seguran\u00e7a Rodovi\u00e1ria e Salvar Vidas<\/a>\u201c.<\/p>\n

    Agradecemos a Lois e Chaitree, e a todos os volunt\u00e1rios do Desafio, por sua valiosa contribui\u00e7\u00e3o!<\/p>","protected":false},"excerpt":{"rendered":"

    Lois Anne Leal and Chaitree Sham Baradkar participated in the innovative iRAP Omdena 8 week Challenge earlier this year to find AI-based solutions to map crash risk and generate iRAP star rating attribute data. 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