{"id":2687,"date":"2021-05-11T11:34:02","date_gmt":"2021-05-11T01:34:02","guid":{"rendered":"https:\/\/wordpress.temp.irap-dev.org\/omdena-challenge-collaborators-share-results\/"},"modified":"2026-04-30T12:38:10","modified_gmt":"2026-04-30T02:38:10","slug":"omdena-challenge-collaborators-share-results","status":"publish","type":"post","link":"https:\/\/irap.org\/pt\/omdena-challenge-collaborators-share-results\/","title":{"rendered":"Colaboradores do Omdena Challenge compartilham resultados"},"content":{"rendered":"

Colaboradores em Desafio Omdena iRAP de 8 semanas<\/a> que tinham como objetivo obter, por meio de colabora\u00e7\u00e3o coletiva, 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, compartilharam alguns dos resultados de suas publica\u00e7\u00f5es.<\/p>\n

No in\u00edcio deste ano, 50 especialistas em IA e aprendizado de m\u00e1quina \u2014 incluindo estudantes em forma\u00e7\u00e3o, cientistas de dados, engenheiros de aprendizado de m\u00e1quina e de dados, al\u00e9m de especialistas na \u00e1rea de todo o mundo \u2014 trabalharam com membros da Equipe de Inova\u00e7\u00e3o e Desenvolvimento do iRAP com o objetivo de encontrar uma solu\u00e7\u00e3o para a coleta e codifica\u00e7\u00e3o aceleradas e inteligentes de dados sobre atributos rodovi\u00e1rios (AiRAP), a fim de prevenir acidentes de tr\u00e2nsito e salvar vidas.<\/p>\n

Leia estes m\u00e9todos Python perspicazes e bem aplicados:<\/p>\n

\"Vehicle-Count-iRAP-1080x675\"<\/p>\n

Usando redes neurais convolucionais para melhorar a seguran\u00e7a rodovi\u00e1ria e salvar vidas\u201d por\u00a0Lois Anne Leal<\/a>\u00a0e\u00a0Chaitree Baradkar<\/a> \u2013 https:\/\/bit.ly\/3gK8men<\/a><\/p>\n

\u201cAn\u00e1lise de Atributos e Machine Learning para Seguran\u00e7a Vi\u00e1ria\u201d por\u00a0Leon Hamnett<\/a>
https:\/\/bit.ly\/3gJGuqt<\/a><\/p>\n

\"Feature\"\"bend13-678x675<\/p>\n

\u201cDetec\u00e7\u00e3o de Objetos em Imagens 2D para Localizar Carros e Pedestres\u201d por\u00a0Giulio Giaconi<\/a>\u00a0e\u00a0Dave Bunten<\/a>
https:\/\/bit.ly\/3vce0cV<\/a><\/p>\n

\u201cAn\u00e1lise Explorat\u00f3ria Avan\u00e7ada de Dados de Seguran\u00e7a Vi\u00e1ria do Reino Unido usando Python\u201d por\u00a0Nabanita Roy<\/a>
https:\/\/bit.ly\/3dY8l4v<\/a><\/p>\n

\"eda20\"stream1\"<\/p>\n

\u201cStreamlit 101: Implanta\u00e7\u00e3o de um Dashboard Streamlit de AutoML para Prever e Exibir Resultados\u201d por\u00a0MUHAMMAD ALI<\/a>
https:\/\/bit.ly\/3vov9QP<\/a><\/p>\n

\u201cAprendendo OpenCV do Zero para Construir um Detector de Pedestres\u201d por\u00a0Rutuja Kawade<\/a>
https:\/\/bit.ly\/2PsvPoS<\/a><\/p>\n

\"opencv7\"<\/p>\n

A iRAP expressa seus sinceros agradecimentos \u00e0 equipe da Omdena. Os resultados servir\u00e3o de base para grandes avan\u00e7os na cria\u00e7\u00e3o de estradas com classifica\u00e7\u00e3o #3starorbetter para todos os usu\u00e1rios, o que salvar\u00e1 vidas.\u00a0<\/p>","protected":false},"excerpt":{"rendered":"

Collaborators on the Omdena iRAP 8 Week Challenge which aimed to crowd source AI-based solutions to map crash risk and generate iRAP star rating attribute data have shared some of their publication results. Earlier this year, 50 AI and machine learning experts including aspiring students, data scientists, ML and data engineers, and domain experts from […]<\/p>\n","protected":false},"author":10,"featured_media":2688,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[28,266],"tags":[147,343,344,148],"class_list":["post-2687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-global-news","category-airap","tag-airap","tag-challenge","tag-innovation","tag-omdena"],"acf":[],"yoast_head":"\nOmdena Challenge collaborators share results - iRAP<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/irap.org\/pt\/omdena-challenge-collaborators-share-results\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Omdena Challenge collaborators share results\" \/>\n<meta property=\"og:description\" content=\"Collaborators on the Omdena iRAP 8 Week Challenge which aimed to crowd source AI-based solutions to map crash risk and generate iRAP star rating attribute data have shared some of their publication results. 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