In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analytical modeling: GAN-based texture generation enables data-driven dirt and wear artifacts, rendering unique and realistic traffic sign surfaces, while the analytical scene modulation achieves physically correct lighting and allows detailed parameterization. In particular, the latter opens up applications in the context of explainable AI (XAI) and robustness tests due to the possibility of evaluating the sensitivity to parameter changes, which we demonstrate with experiments. Our resulting synthetic traffic sign recognition dataset Synset Signset Germany contains a total of 105500 images of 211 different German traffic sign classes, including newly published (2020) and thus comparatively rare traffic signs. In addition to a mask and a segmentation image, we also provide extensive metadata including the stochastically selected environment and imaging effect parameters for each image. We evaluate the degree of realism of Synset Signset Germany on the real-world German Traffic Sign Recognition Benchmark (GTSRB) and in comparison to CATERED, a state-of-the-art synthetic traffic sign recognition dataset.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition


    Contributors:


    Publication date :

    2024-09-24


    Size :

    4618021 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Synset Signset Germany: Synthetischer Bilddatensatz für Verkehrszeichenerkennung

    Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. | Mobilithek

    Free access

    Synset Boulevard: Synthetischer Bilddatensatz für Vehicle Make and Model Recognition (VMMR)

    Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. | Mobilithek

    Free access

    German Traffic Sign Recognition Using Convolutional Neural Network

    Santosh, G V S Sree / Kumar, G Chaitanya / Sandeep, G et al. | IEEE | 2022


    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    MIYASATO KAZUHIRO / KOYASU TOSHIYA | European Patent Office | 2023

    Free access

    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    SHINOMIYA TERUHIKO | European Patent Office | 2017

    Free access