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A depth map helps robots and autonomous vehicles (AVs) visualize the three-dimensional world to navigate and localize neighboring obstacles. However, it is difficult to develop a deep learning model that can estimate the depth map from a single image in real-time. This study proposes a fast monocular depth estimation model named FastMDE by optimizing the deep convolutional neural network according to the encoder-decoder architecture. The decoder needs to obtain partial and semantic feature mapsdoi:10.1109/access.2022.3145969 fatcat:hsf5y25otbgtlfmcaq3haw2e4q