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IMAGE SEGMENTATION ACCURACY DEPENDING ON THE DEPTH OF U-NET MODEL

Jevģēnijs Riekstiņš, Sergejs Kodors

Abstract


The aim of this work is to obtain information about impact of the depth of U-Net architecture model into segmentation accuracy. Experiment was completed using dataset of DSM images. Neural networks were trained to recognize building locations. Experiment considered to decrease the number of U-Net filter blokes to measure impact on result accuracy.

Keywords


accuracy; image segmentation; machine learning; neural network

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References


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DOI: https://doi.org/10.17770/het2020.24.6755

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