Deep Neural Networks trained in a fully supervised fashion are the dominant technology in perception-based autonomous driving systems. While collecting large amounts of unlabeled data is already a major undertaking, only a subset of it can be labeled by humans due to the effort needed for high-quality annotation. Therefore, finding the right data to label has become a key challenge. Active learning is a powerful technique to improve data efficiency for supervised learning methods, as it aims at selecting the smallest possible training set to reach a required performance. We have built a scalable production system for active learning in the domain of autonomous driving. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, present our current results at scale, and briefly describe the open problems and future directions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Scalable Active Learning for Object Detection


    Contributors:


    Publication date :

    2020-10-19


    Size :

    3312748 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SCALABLE ACTIVE LEARNING FOR OBJECT DETECTION

    Haussmann, Elmar / Fenzi, Michele / Chitta, Kashyap et al. | British Library Conference Proceedings | 2020


    Scalable Object Detection Solution for Vehicular Applications

    Thakur, Kunal / Soni, Dhruv / Taneja, Ashu | IEEE | 2024


    Advanced Active Learning Strategies for Object Detection

    Schmidt, Sebastian / Rao, Qing / Tatsch, Julian et al. | IEEE | 2020


    ADVANCED ACTIVE LEARNING STRATEGIES FOR OBJECT DETECTION

    Schmidt, Sebastian / Rao, Qing / Tatsch, Julian et al. | British Library Conference Proceedings | 2020


    Learning Active Basis Model for Object Detection and Recognition

    Wu, Y. N. / Si, Z. / Gong, H. et al. | British Library Online Contents | 2010