Aiming at the problem of low accuracy in monocular vision image depth estimation

a monocular image depth estimation method based on Transformer and convolutional neural network is proposed. First

ResNet-50 is used as the backbone network of the encoder-decoder network to extract image features. At the same time

the encoder-decoder network adopts a level fusion method to fuse the features of each level of the encoder as the decoder to input to improve the utilization of multi-scale feature information by the depth estimation network. Secondly

the Transformer network is used to perform global analysis on the output features of the decoder. The multi-head attention mechanism in the Transformer network estimates the depth information from the deep features output by the decoder

which improves the depth estimation network′s ability to extract multi-scale features and thus improves the depth map accuracy. The validation of the model was completed on the NYU Depth-v2 dataset. Experimental results show that compared with multi-scale convolutional neural networks

this method is improved by 19.9% in δ＜1.25 and root mean square error is reduced by 49.9%. The feasibility of proposed method in estimating depth from a monocular image is proved.