A self-supervised method to train a visual predictor of drivable surface roughness in front of a vehicle is proposed. A convolutional neural network taking a single camera image is trained on a dataset labeled automatically by a cross-modal supervision. The dataset is collected by driving a vehicle on various surfaces, while synchronously recording images and accelerometer data. The surface images are labeled by the local roughness measured using the accelerometer signal aligned in time. Our experiments show that the proposed training scheme results in accurate visual predictor. The correlation coefficient between the visually predicted roughness and the true roughness (measured by the accelerometer) is 0.9 on our independent test set of about 1000 images. The proposed method clearly outperforms a baseline method which has the correlation of 0.3 only. The baseline is based on surface texture strength without any training. Moreover, we show a coarse map of local surface roughness, which is implemented by scanning an input image with the trained convolutional network. The proposed method provides automatic and objective road condition assessment, enabling a cheap and reliable alternative to manual data annotation, which is infeasible in a large scale.


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    Title :

    Self-Supervised Learning of Camera-based Drivable Surface Roughness


    Contributors:
    Cech, Jan (author) / Hanis, Tomas (author) / Kononisky, Adam (author) / Rurtle, Tomas (author) / Svancar, Jan (author) / Twardzik, Tomas (author)


    Publication date :

    2021-07-11


    Size :

    5637351 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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