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                <identifier>oai:data.mendeley.com/y6zyrnxbfm.4</identifier>
                <datestamp>2021-12-08T14:55:59Z</datestamp>
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    <dc:creator>Bhutad, Sonali</dc:creator>
    <dc:title>Dataset of Stagnant Water and Wet Surface with Annotations</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>Stagnant Water detection is a very challenging task due to mudding or reflection of surrounding . Therefore, to increase the accuracy of stagnant water detection by avoiding misclassification; additional images of wet surface are added. With this objective, we have created a dataset of 1976 images with annotations. The dataset consists of RGB labeled images (256 × 256 pixels) for two classes, namely water and wet surface. The images were taken from the top view and side view with varying daylight conditions.  The rear camera of a mobile phone is used to capture images. The annotations are in the YOLO (You Only Look Once) format.</dc:description>
    <dc:subject>Computer Vision</dc:subject>
    <dc:subject>Object Detection</dc:subject>
    <dc:subject>Object Recognition</dc:subject>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Convolutional Neural Network</dc:subject>
    <dc:subject>Deep Learning</dc:subject>
    <dc:contributor>Patil, Kailas </dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/y6zyrnxbfm.4</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/y6zyrnxbfm.4</dc:identifier>
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    <dc:date>2021-12-08T14:55:59Z</dc:date>
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