{"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":"Os colaboradores do Desafio Omdena compartilham os resultados."},"content":{"rendered":"

Colaboradores no Desafio de 8 semanas Omdena iRAP<\/a> que visavam obter solu\u00e7\u00f5es baseadas em IA por meio de crowdsourcing para mapear o risco de acidentes e gerar dados de atributos de classifica\u00e7\u00e3o por estrelas de 1 a 5 estrelas, compartilharam alguns dos resultados de sua publica\u00e7\u00e3o.<\/p>\n

No in\u00edcio deste ano, 50 especialistas em IA e aprendizado de m\u00e1quina, incluindo estudantes promissores, cientistas de dados, engenheiros de ML e de dados e especialistas da \u00e1rea de todo o mundo, trabalharam com membros da Equipe de Inova\u00e7\u00e3o e Desenvolvimento do iRAP, com foco na busca de uma solu\u00e7\u00e3o para a coleta e codifica\u00e7\u00e3o acelerada e inteligente de dados de atributos de estradas (AiRAP) para prevenir acidentes rodovi\u00e1rios e salvar vidas.<\/p>\n

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

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

"Utilizando Redes Neurais Convolucionais para Melhorar a Seguran\u00e7a Rodovi\u00e1ria e Salvar Vidas" por\u00a0Lois Anne Leal<\/a>\u00a0e\u00a0Chaitree Baradkar<\/a> \u2013 https:\/\/bit.ly\/3gK8men<\/a><\/p>\n

\u201cAn\u00e1lise de Caracter\u00edsticas e Aprendizado de M\u00e1quina para Seguran\u00e7a Rodovi\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 de Dados Avan\u00e7ada dos Dados de Seguran\u00e7a Rodovi\u00e1ria do Reino Unido usando Python\u201d por\u00a0Nabanita Roy<\/a>
https:\/\/bit.ly\/3dY8l4v<\/a><\/p>\n

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

\u201cStreamlit 101: Implantando um painel Streamlit com 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 contribuir\u00e3o significativamente para o projeto #3star, que visa melhorar as estradas para todos os usu\u00e1rios e salvar 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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