{"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\/nl\/omdena-challenge-collaborators-share-results\/","title":{"rendered":"Medewerkers van de Omdena Challenge delen resultaten"},"content":{"rendered":"

Medewerkers aan de Omdena iRAP 8 weken durende uitdaging<\/a> Een project dat tot doel had om via crowdsourcing AI-gebaseerde oplossingen te ontwikkelen voor het in kaart brengen van het risico op verkeersongevallen en het genereren van gegevens voor een iRAP-sterrenclassificatie, heeft een deel van de resultaten van hun publicatie gedeeld.<\/p>\n

Eerder dit jaar werkten 50 experts op het gebied van AI en machine learning, waaronder ambitieuze studenten, datawetenschappers, ML- en data-engineers en domeinexperts van over de hele wereld, samen met leden van het iRAP Innovatie- en Ontwikkelingsteam aan het vinden van een oplossing voor het versneld en intelligent verzamelen en coderen van wegattribuutgegevens (AiRAP) om verkeersongevallen te voorkomen en levens te redden.<\/p>\n

Lees deze inzichtelijke en goed toegepaste Python-methoden:<\/p>\n

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

"Het gebruik van convolutionele neurale netwerken om de verkeersveiligheid te verbeteren en levens te redden" door\u00a0Lois Anne Leal<\/a>\u00a0En\u00a0Chaitree Baradkar<\/a> \u2013 https:\/\/bit.ly\/3gK8men<\/a><\/p>\n

\u201cFunctieanalyse en machinaal leren voor verkeersveiligheid\u201d door\u00a0Leon Hamnett<\/a>
https:\/\/bit.ly\/3gJGuqt<\/a><\/p>\n

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

\u201cObjectdetectie in 2D-afbeeldingen om auto's en voetgangers te lokaliseren\u201d door\u00a0Giulio Giaconi<\/a>\u00a0En\u00a0Dave Bunten<\/a>
https:\/\/bit.ly\/3vce0cV<\/a><\/p>\n

\u201cGeavanceerde EDA van Britse verkeersveiligheidsgegevens met behulp van Python\u201d door\u00a0Nabanita Roy<\/a>
https:\/\/bit.ly\/3dY8l4v<\/a><\/p>\n

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

\u201cStreamlit 101: Een AutoML Streamlit-dashboard implementeren om resultaten te voorspellen en weer te geven\u201d door\u00a0MUHAMMAD ALI<\/a>
https:\/\/bit.ly\/3vov9QP<\/a><\/p>\n

\u201cOpenCV helemaal vanaf nul leren om een voetgangersdetector te bouwen\u201d door\u00a0Rutuja Kawade<\/a>
https:\/\/bit.ly\/2PsvPoS<\/a><\/p>\n

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

iRAP betuigt zijn oprechte dank aan het Omdena-team. De resultaten zullen leiden tot grote stappen voorwaarts voor #3staror, betere wegen voor alle weggebruikers en daarmee tot het redden van levens.\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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