Research on monocular depth estimation algorithm based on improved DNA-Depth

DNA-Depth is currently one of the better-performing lightweight models for monocular depth estimation, but predicting detailed information remains challenging. This paper proposes a method called EUF-Depth, which can efficiently fuse encoder features based on DNA-Depth. Firstly, a feature fusion strategy is introduced to improve the utilization of important features through a learnable method. Then, the channel attention in the decoder part is replaced with Coordinate Attention (CA), incorporating positional information to enhance model prediction accuracy. Experiments on the KITTI benchmark demonstrate that EUF-Depth outperforms DNA-Depth in all metrics. The error evaluation metrics decrease by an average of 2% and the accuracy evaluation metrics increase by an average of 1%.