Adverse weather conditions can negatively affect LiDAR-based object detectors. In this work, we focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditions. This everyday effect can influence the estimation of object sizes, orientations and introduce ghost object detections, compromising the reliability of the state of the art object detectors. We propose to solve this problem by using data augmentation and a novel training loss term. To effectively train deep neural networks, a large set of labeled data is needed. In case of adverse weather conditions, this process can be extremely laborious and expensive. We address this issue in two steps: First, we present a gas exhaust data generation method based on 3D surface reconstruction and sampling which allows us to generate large sets of gas exhaust clouds from a small pool of labeled data. Second, we introduce a point cloud augmentation process that can be used to add gas exhaust to datasets recorded in good weather conditions. Finally, we formulate a new training loss term that leverages the augmented point cloud to increase object detection robustness by penalizing predictions that include noise. In contrast to other works, our method can be used with both grid-based and point-based detectors. Moreover, since our approach does not require any network architecture changes, inference times remain unchanged. Experimental results on real data show that our proposed method greatly increases robustness to gas exhaust and noisy data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Robust 3D Object Detection in Cold Weather Conditions


    Beteiligte:
    Piroli, Aldi (Autor:in) / Dallabetta, Vinzenz (Autor:in) / Walessa, Marc (Autor:in) / Meissner, Daniel (Autor:in) / Kopp, Johannes (Autor:in) / Dietmayer, Klaus (Autor:in)


    Erscheinungsdatum :

    2022-06-05


    Format / Umfang :

    4144781 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    ENGINE LUBRICATION UNDER COLD WEATHER CONDITIONS

    Zucrow, M. J. | SAE Technical Papers | 1938


    MWNet: object detection network applicable for different weather conditions

    Pei, Liu / Yuan, Xue / Dai, XueRui | Wiley | 2019

    Freier Zugriff

    Object Detection System in Adverse Weather Conditions Using Ann

    Joshua, Jeberson A / Narasiman, L / Yogeshwaran, V et al. | IEEE | 2023


    MWNet: object detection network applicable for different weather conditions

    Pei, Liu / Yuan, Xue / Dai, XueRui | IET | 2019

    Freier Zugriff

    Coatings for aircraft in cold weather conditions

    Lakio-Haapio, M. | Tema Archiv | 1987