The proposal of MonoDepth2 has made significant progress in self-supervised monocular depth estimation,but the prediction effect of the network in large non semantic regions and boundaries is not ideal.The main reason is that the basic U-Net framework does not make full use of multi-scale feature information,resulting in poor depth estimation from large gradient regions.To address this problem,this paper proposed an improved DepthNet,a hierarchical integration net (HINet).The U-Net network structure is optimized so that the encoder side can generate feature information of different scales at each layer,thus allowing the decoder side to fully fuse multi-scale features at each layer.Since the feature information of different scales contributes to a specific decoder layer to different degrees,the hierarchical integration (HINet) algorithm proposed in this paper also adds a channel attention module to enhance the weight of important feature scales.When stereo pairs are used for training,this paper preprocesses the data and adds a depth-implying loss function for stereo pairs.The experimental results on the KITTI dataset show that all indicators are improved to varying degrees,in which the absolute relative error is reduced by 0.09 and the squared relative error is reduced by 0.093.