Highway pavement health-condition and maintenance is crucial for traffic safety. Through our investigation, Prevalent methods still can not provide satisfactory results in highway pavement defect detection due to the diversity and complexity of the defects. In order to address the diversity and complexity of defects and enhance the performance and efficiency of highway defect detection, we propose a novel pavement defects detection framework termed HPDD (Highway Pavement Defect Detection)-Net. The proposed model adopts the Swin transformer as the backbone, FPN (Feature Pyramid Networks)as the neck, and the TOOD module as the Bbox_head. HPDD-Net integrates candidate region generation and object classification tasks through task alignment. Compared to prevalent two-staged detection models, our approach offers significant improvements in terms of speed and precision. Experimental results has again proved the effectiveness of our implementation by achieving excellent image classification performance and computational efficiency.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Improving Highway Pavement Defect Detection via Swin Transformer Integrated TOOD Model


    Beteiligte:
    Zhao, Yi (Autor:in) / Zhou, Buyun (Autor:in) / Wang, Liang (Autor:in) / An, Yong (Autor:in)


    Erscheinungsdatum :

    28.10.2023


    Format / Umfang :

    4425727 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    UAV Vehicle Detection Method and Application Based on Improved TOOD

    Ou, Kaitong / Dong, Chaojun / Huang, Wanxia et al. | IEEE | 2023


    Real-Time Detection of Voids in Asphalt Pavement Based on Swin-Transformer-Improved YOLOv5

    Zhang, Bei / Cheng, Haoyuan / Zhong, Yanhui et al. | IEEE | 2024


    Improving Road Extraction for Autonomous Driving Using Swin Transformer Unet

    Ge, Chao / Nie, Yiming / Kong, Fanjie et al. | IEEE | 2022


    Object Detection in Maritime Scenarios Based on Swin-Transformer

    Sun, Wenli / Gao, Xu | Springer Verlag | 2022