Object detection is a fundamental task in autonomous driving. Besides bounding-box-like detection algorithms, uncertainty estimation is necessary for safe and trust-worthy perceptions. Bayesian Neural Networks (BNNs) provide a reliable approach to address the challenge. However, it often becomes computationally prohibitive to apply them to modern large-scale neural networks. This work develops an efficient BNN by combining the Laplace Approximation (LA) with linearized inference. Specifically, we study the effectiveness and computational necessity of a diagonal Hessian approximation in the LA on over-parameterized networks. With numerous quantitative experiments on different types of interference, the proposed method demonstrates the ability for real-time and robust uncertainty description for autonomous driving.


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    Titel :

    Laplace Approximation for Real-Time Uncertainty Estimation in Object Detection


    Beteiligte:
    Gui, Ming (Autor:in) / Qiu, Tianming (Autor:in) / Bauer, Fridolin (Autor:in) / Shen, Hao (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    2603304 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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