{"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":"Usando Redes Neurais Convolucionais Para Melhorar a Seguran\u00e7a Vi\u00e1ria e Salvar Vidas"},"content":{"rendered":"

Lois Anne Leal e Chaitree Sham Baradkar participaram da inovadora Desafio Omdena de 8 semanas iRAP<\/a> no in\u00edcio deste ano para encontrar solu\u00e7\u00f5es baseadas em IA para mapear o risco de acidentes e gerar dados de atributos para a classifica\u00e7\u00e3o por estrelas iRAP. Eles eram apenas dois dos 50 engenheiros de IA e aprendizado de m\u00e1quina recrutados por meio de crowdsourcing em todo o mundo, com o objetivo de encontrar uma solu\u00e7\u00e3o para a coleta e codifica\u00e7\u00e3o aceleradas e inteligentes de dados de atributos rodovi\u00e1rios (AiRAP<\/a>) para salvar vidas.<\/p>\n

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

O Desafio Omdena iRAP tinha tr\u00eas objetivos principais:<\/p>\n

    \n
  1. Obter dados geolocalizados de acidentes e elaborar mapas de risco iRAP com o n\u00famero de acidentes hist\u00f3ricos por quil\u00f4metro e o n\u00famero de acidentes por quil\u00f4metro percorrido para cada usu\u00e1rio da via<\/li>\n
  2. Atribuir dados sobre caracter\u00edsticas das vias, fluxo de tr\u00e1fego e velocidade de acordo com o padr\u00e3o global iRAP e mapear o desempenho em 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) para infraestrutura rodovi\u00e1ria que sejam repet\u00edveis e que possam formar a base para o acompanhamento de desempenho anual<\/li>\n<\/ol>\n

    Lois e Chaitree contribu\u00edram para o segundo objetivo ao obterem automaticamente o componente crucial da contagem de ve\u00edculos sob o atributo de tr\u00e1fego ou fluxo de ve\u00edculos usando imagens de sat\u00e9lite com a ajuda da Intelig\u00eancia Artificial.<\/p>\n

    Leia o blog publicado deles: \u201cUsando Redes Neurais Convolucionais Para Melhorar a Seguran\u00e7a Vi\u00e1ria e Salvar Vidas<\/a>\u201c.<\/p>\n

    Nossos agradecimentos 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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