Deep Learning methods are widely applied in Robotics and Automated Driving scenarios. The task of perception for Automated Driving in the real world is particularly challenging and requires a sufficient amount of high quality labeled training data for the algorithms to perform well. However, the means of obtaining real world datasets are limited. It is common practice to have human labelers involved at least to some extent. Regardless of whether the process is partially automated or not, these labels never represent perfectly accurate ground-truth. By investigating the recognition performance of a state-of-the-art object detector as a function of the quality of a labeled real world training set, we study the effect of labeling errors of various types and severity. To this end, the given labels are treated as a reference to which synthetic errors are added systematically in order to determine the performance of the object detector if trained on the erroneous dataset.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Estimating Labeling Quality with Deep Object Detectors


    Contributors:


    Publication date :

    2019-06-01


    Size :

    1497741 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Object detectors emerge in Deep Scene CNNs

    Zhou, Bolei / Khosla, Aditya / Lapedriza Garcia, Agata et al. | DSpace@MIT | 2015

    Free access

    Robustness and Deployability of Deep Object Detectors in Autonomous Driving

    Adam, Ganesh / Chitalia, Vibhav / Simha, Namrata et al. | IEEE | 2019


    Uncertainty Estimation for Deep Neural Object Detectors in Safety-Critical Applications

    Le, Michael Truong / Diehl, Frederik / Brunner, Thomas et al. | IEEE | 2018


    Training Object Detectors With Noisy Data

    Chadwick, Simon / Newman, Paul | IEEE | 2019


    OBJECT DISTANCE ESTIMATING DEVICE AND OBJECT DISTANCE ESTIMATING METHOD

    HARA HIDEYUKI | European Patent Office | 2021

    Free access