Accurate and timely surveying of airfield pavement distress is crucial for cost-effective airport maintenance. Deep learning (DL) approaches, leveraging advancements in computer science and image acquisition techniques, have become the mainstream for automated airfield pavement distress detection. However, fully-supervised DL methods require a large number of manually annotated ground truth labels to achieve high accuracy. To address the challenge of limited high-quality manual annotations, we propose a novel end-to-end distress detection model called class activation map informed weakly-supervised distress detection (WSDD-CAM ). Based on YOLOv5, WSDD-CAM consists of an efficient backbone, a classification branch, and a localization network. By utilizing class activation map (CAM) information, our model significantly reduces the need for manual annotations, automatically generating pseudo bounding boxes with a 71% overlap with the ground truth. To evaluate WSDD-CAM, we tested it on a self-made dataset and compared it with other weakly-supervised and fully-supervised models. The results show that our model achieves 49.2% mean average precision (mAP), outperforming other weakly-supervised methods and even approaching state-of-the-art fully-supervised methods. Additionally, ablation experiments confirm the effectiveness of our architecture design. In conclusion, our WSDD-CAM model offers a promising solution for airfield pavement distress detection, reducing manual annotation time while maintaining high accuracy. This efficient and effective approach can significantly contribute to cost-effective airport maintenance management.


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    Titel :

    A weakly-supervised deep learning model for end-to-end detection of airfield pavement distress


    Beteiligte:
    Zefeng Tao (Autor:in) / Hongren Gong (Autor:in) / Liming Liu (Autor:in) / Lin Cong (Autor:in) / Haimei Liang (Autor:in)


    Erscheinungsdatum :

    2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    A weakly-supervised deep learning model for end-to-end detection of airfield pavement distress

    Tao, Zefeng / Gong, Hongren / Liu, Liming et al. | Elsevier | 2025

    Freier Zugriff

    Airfield pavement design

    Fyson, J.H. | Engineering Index Backfile | 1962


    Airfield Pavement Systems

    E. L. Murphree / R. W. Woodhead / R. H. Wortman | NTIS | 1971



    Airfield Pavement Condition Assessment

    Greene, J. / Shahin, M. Y. / Alexander, D. R. | Transportation Research Record | 2004