Reference number : 172-2021-2022-Q1-993367, amendment number : 2, agreement type : G, recipient legal name : Deutsches forschungszentrum für künstliche intelligenz gmbh, recipient country : DE, recipient province : nan, recipient city : KAISERSLAUTERN, recipient postal code : 67663, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Machine learning identification of pollutants and other debris in Canadian waterways, agreement title french : Détermination des polluants et des débris dans les cours d’eau canadiens par apprentissage automatique, agreement number : 993367, agreement value : 99990.0, agreement start date : 2022-06-01, agreement end date : 2024-10-31, description english : The collaborators aim to enhance an AI-based computer vision program so it can be deployed to monitor Canadian waterways and identify problem areas for plastic pollution in need of remediation. The proposed project takes Canadian (NRC) expertise in computer vision for the identification of ice and pairs that with German (DFKI) expertise in computer vision expertise in the identification of plastic to meet the Oceans Challenge Program's need for pollution remediation. The project takes an existing German computer vision program that can identify plastic in ice free waterways and applys it to Canadian waters. Because a computer vision algorithm trained to identify plastic in rivers could be confused by floating ice, it is seen as a necessary improvement to the model to train it to distinct between ice and plastic litter. The outcomes of the project can add benefit of helping to understand plastic waste situation in Canadian rivers., description french : Détermination des polluants et des débris dans les cours d’eau canadiens par apprentissage automatique, expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : This agreement has been amended 2 time(s). The end date of this agreement has been modified by 213 days., additional information french : Cet accord a été modifié 2 fois.  La date de fin du présent accord a été changé de 213 jours., owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada