Manual annotation of soiling on surround view cameras is a very challenging and expensive task. The unclear boundary for various soiling categories like water drops or mud particles usually results in a large variance in the annotation quality. As a result, the models trained on such poorly annotated data are far from being optimal. In this paper, we focus on handling such noisy annotations via pseudo-label driven ensemble model which allow us to quickly spot problematic annotations and in most cases also sufficiently fixing them. We train a soiling segmentation model on both noisy and refined labels and demonstrate significant improvements using the refined annotations. It also illustrates that it is possible to effectively refine lower cost coarse annotations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Ensemble-Based Semi-Supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving


    Beteiligte:
    Uricar, Michal (Autor:in) / Sistu, Ganesh (Autor:in) / Yahiaoui, Lucie (Autor:in) / Yogamani, Senthil (Autor:in)


    Erscheinungsdatum :

    2021-09-19


    Format / Umfang :

    1783406 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles

    Altche, Florent / Qian, Xiangjun / de La Fortelle, Arnaud | IEEE | 2017




    Using Predictive Uncertainty for Cleaning Noisy Annotations

    Gütter, Jonas Aaron / Ulman, Hannah / Niebling, Julia | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2022

    Freier Zugriff