To develop robust and secure automated transportation systems, Traffic Sign Detection and Recognition (TSDR) is a key part. It plays a crucial role in Advanced Driver Assistance Systems (ADAS), self-driving vehicles and traffic safety. However, the task of TSDR can be challenging due to traffic signs being subject to damages, discoloration, vandalism and occlusion. Even though a lot of progress is made in both research areas of Traffic Sign Detection (TSD) and Traffic Sign Recognition (TSR), no study explicitly deals with the problem of qualitative poor traffic signs appearing in real-world scenarios. This can be assigned to the lack of an extensive traffic sign dataset containing flawless signs as well as imperfect signs. Neural networks trained exclusively on untainted data might fail at detecting flawed signs as they occur in real-world scenarios. Therefore, in this paper, a novel traffic sign dataset with condition annotations is proposed, indicating if a sign is good, discolored, vandalized, dirty or occluded. The custom dataset is created with a semi-supervised approach, in which machine learning models are trained to classify traffic signs in the condition categories. The resulting dataset can be used as basis for more precise traffic sign recognition as well as traffic sign condition classification which can be useful for maintenance planning. The dataset includes approx. 20.000 images of 10 sign classes, where 70% of data is incorporated in the training set, 10% in the validation set and 20% in the test set.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A novel Traffic Sign Dataset with Condition Annotations


    Contributors:


    Publication date :

    2023-12-05


    Size :

    1288080 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic road condition display sign and road condition display system

    CHEN LIHUA / SUN JIGANG / QIAN YUANYUAN | European Patent Office | 2023

    Free access

    GLARE: A Dataset for Traffic Sign Detection in Sun Glare

    Gray, Nicholas / Moraes, Megan / Bian, Jiang et al. | IEEE | 2023


    Real-Time Traffic Sign Detection Under Foggy Condition

    Anthony, Renit / Biswas, Jayanta | Springer Verlag | 2022


    GLARE: A Dataset for Traffic Sign Detection in Sun Glare

    Gray, Nicholas / Moraes, Megan / Bian, Jiang et al. | ArXiv | 2022

    Free access

    Intelligent traffic road condition violation prohibition sign storage device

    WANG JIANGTAO | European Patent Office | 2015

    Free access