Feature maps of different scales in convolutional neural networks (CNNs) can be regarded as image pyramids. In classification tasks, only the last layer of feature maps is used for making decision. However, in tasks such as road crack detection, the target objects are so small that some original information might lose during the downsampling process in CNN. The authors propose a structure that uses the information that is contained in different layers of feature maps, so all the information could contribute to the classification. This process is managed by adding the weighted values of pixels in corresponding regions of different layers in feature maps and using the sum of these values as the output. The authors apply this structure on a residual network and use it to learn the features of road cracks. Experiments have shown that with the authors’ structure, the network performs better than others at understanding and detecting road cracks.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Multi‐scale classification network for road crack detection


    Beteiligte:
    Feng, Hui (Autor:in) / Xu, Guo‐sheng (Autor:in) / Guo, Yanhui (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2019-02-01


    Format / Umfang :

    8 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Multi-scale classification network for road crack detection

    Feng, Hui / Xu, Guo-sheng / Guo, Yanhui | IET | 2018

    Freier Zugriff

    Intelligent Graph Convolutional Neural Network for Road Crack Detection

    Djenouri, Youcef / Belhadi, Asma / Houssein, Essam H. et al. | IEEE | 2023


    Crack detection system for road

    Europäisches Patentamt | 2024

    Freier Zugriff

    Crack detection system for road

    DO MYUNG SIK / CHOI SEUNG HYUN / HYUN SE KWON | Europäisches Patentamt | 2023

    Freier Zugriff

    DMF-Net: A Dual-Encoding Multi-Scale Fusion Network for Pavement Crack Detection

    Bai, Suli / Yang, Lei / Liu, Yanhong et al. | IEEE | 2024