  Monocular depth estimation can play an important role in addressing the issue
of deriving scene geometry from 2D images. It has been used in a variety of
industries, including robots, self-driving cars, scene comprehension, 3D
reconstructions, and others. The goal of our method is to create a lightweight
machine-learning model in order to predict the depth value of each pixel given
only a single RGB image as input with the Unet structure of the image
segmentation network. We use the NYU Depth V2 dataset to test the structure and
compare the result with other methods. The proposed method achieves relatively
high accuracy and low rootmean-square error.
