In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements in various studies on computer vision and deep learning. Although large-scale and well-annotated datasets enhance the performance of detecting road defects to some extent, it is still challengeable to derive a model which can perform reliably for various road conditions in practice, because it is intractable to construct a dataset considering diverse road conditions and defect patterns. To end this, we propose an unsupervised approach to detect road defects, using Adversarial Image-to-Frequency Transform (AIFT). AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not require annotations for road defects. We evaluate the efficiency of AIFT using GAPs384 dataset, Cracktree200 dataset, CRACK500 dataset, and CFD dataset. The experimental results demonstrate that the proposed approach detects various road detects, and it outperforms existing state-of-the-art approaches.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform


    Beteiligte:
    Yu, Jongmin (Autor:in) / Kim, Du Yong (Autor:in) / Lee, Younkwan (Autor:in) / Jeon, Moongu (Autor:in)


    Erscheinungsdatum :

    2020-10-19


    Format / Umfang :

    3144336 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-class Pixel Level Segmentation for Drivable Road Detection

    Sandhya, S. / Awadhiya, Mohini / Nimmala, Bhavani et al. | Springer Verlag | 2024



    Design gabor filters in the frequency domain for unsupervised fabric defect detection

    Zhang, Xingye / Pan, Ruru / Liu, Jihong et al. | Tema Archiv | 2011


    An adaptive level-selecting wavelet transform for texture defect detection

    Han, Y. / Shi, P. | British Library Online Contents | 2007


    Efficient Road Crack Detection Based on an Adaptive Pixel-Level Segmentation Algorithm

    Safaei, Nima / Smadi, Omar / Safaei, Babak et al. | Transportation Research Record | 2021