{"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\/fr\/omdena-challenge-collaborators-share-results\/","title":{"rendered":"LesBook, Omdena 2023 Challenge participants partagent leurs r\u00e9sultats"},"content":{"rendered":"

Les collaborateurs sur le D\u00e9fi Omdena iRAP de 8 semaines<\/a> qui visaient \u00e0 recourir au crowdsourcing pour trouver des solutions bas\u00e9es sur l'IA permettant de cartographier les risques d'accident et de g\u00e9n\u00e9rer des donn\u00e9es sur les notes attribu\u00e9es selon le syst\u00e8me \u00ab iRAP \u00bb, ont partag\u00e9 certains des r\u00e9sultats de leurs publications.<\/p>\n

Au d\u00e9but de cette ann\u00e9e, 50 experts en IA et en apprentissage automatique, parmi lesquels des \u00e9tudiants en formation, des data scientists, des ing\u00e9nieurs en apprentissage automatique et en donn\u00e9es, ainsi que des experts sectoriels du monde entier, ont collabor\u00e9 avec les membres de l\u2019\u00e9quipe Innovation et D\u00e9veloppement de iRAP afin de trouver une solution permettant la collecte et le codage acc\u00e9l\u00e9r\u00e9s et intelligents des donn\u00e9es relatives aux caract\u00e9ristiques routi\u00e8res (AiRAP), dans le but de pr\u00e9venir les accidents de la route et de sauver des vies.<\/p>\n

Lisez ces m\u00e9thodes Python perspicaces et bien appliqu\u00e9es :<\/p>\n

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

\u201d L'utilisation des r\u00e9seaux neuronaux convolutifs pour am\u00e9liorer la s\u00e9curit\u00e9 routi\u00e8re et sauver des vies \u00bb par\u00a0Lois Anne Leal<\/a>\u00a0et\u00a0Chaitree Baradkar<\/a> \u2013 https:\/\/bit.ly\/3gK8men<\/a><\/p>\n

\u201c Analyse de caract\u00e9ristiques et apprentissage automatique pour la s\u00e9curit\u00e9 routi\u00e8re \u201d par\u00a0L\u00e9on Hamnett<\/a>
https:\/\/bit.ly\/3gJGuqt<\/a><\/p>\n

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

\u201c D\u00e9tection d'objets dans des images 2D pour localiser des voitures et des pi\u00e9tons \u201d par\u00a0Giulio Giaconi<\/a>\u00a0et\u00a0Dave Bunten<\/a>
https:\/\/bit.ly\/3vce0cV<\/a><\/p>\n

\u201c Exploration approfondie des donn\u00e9es de s\u00e9curit\u00e9 routi\u00e8re du Royaume-Uni \u00e0 l'aide de Python \u201d par\u00a0Nabanita Roy<\/a>
https:\/\/bit.ly\/3dY8l4v<\/a><\/p>\n

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

\u201c Streamlit 101 : D\u00e9ployer un tableau de bord Streamlit AutoML pour pr\u00e9dire et afficher les r\u00e9sultats \u201d par\u00a0MUHAMMAD ALI<\/a>
https:\/\/bit.ly\/3vov9QP<\/a><\/p>\n

\u201c Apprendre OpenCV \u00e0 partir de z\u00e9ro pour construire un d\u00e9tecteur de pi\u00e9tons \u201d par\u00a0Rutuja Kawade<\/a>
https:\/\/bit.ly\/2PsvPoS<\/a><\/p>\n

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

iRAP tient \u00e0 adresser ses sinc\u00e8res remerciements \u00e0 l'\u00e9quipe d'Omdena. Ces r\u00e9sultats permettront de franchir des \u00e9tapes d\u00e9cisives vers la mise en place de routes de cat\u00e9gorie #3starorbetter pour tous les usagers de la route, ce qui permettra de sauver des vies.\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. 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