Multi-scale Monocular Depth Estimation Network Based on Channel Attention

‍ ‍Existing monocular depth estimation algorithms have suffered from inaccurate detail estimation and incorrect estimation of distances in the same plane. The depth information is estimated from the three-channel information of the image pixels， but the influence of the interrelationship among the feature map channels on the depth information is rarely considered in the currently available algorithms. Therefore， this paper proposes the SE-DenseDepth network， which embeds a channel attention mechanism in the encoder of the network to encode channels based on the difference in the contribution of different channels to the depth information to improve the encoder's ability to characterize image features. To obtain the detailed depth information of the image， the network establishes an encoder-to-decoder skip connection that introduces more low-level information. In this paper， we train on the generic indoor dataset NYU-Depth V2 and test on real data. Experimental results show that the proposed method can estimate the depth more accurately in regions where the depth changes abruptly. Meanwhile， the situation where depth estimation in a large plan suddenly changes will not occur. Compared with other mainstream algorithms， the proposed method can achieve better depth estimation performance.