Aiming at the problem of inaccurate prediction of edges and the farthest region in monocular image depth estimation, a monocular depth estimation method based on Pyramid Split attention Network (PS-Net) was proposed. Firstly, based on Boundary-induced and Scene-aggregated Network (BS-Net), Pyramid Split Attention (PSA) module was introduced in PS-Net to process the spatial information of multi-scale features and effectively establish the long-term dependence between multi-scale channel attentions, thereby extracting the boundary with sharp change depth gradient and the farthest region. Then, the Mish function was used as the activation function in the decoder to further improve the performance of the network. Finally, training and evaluation were performed on NYUD v2 (New York University Depth dataset v2) and iBims-1 (independent Benchmark images and matched scans v1) datasets. Experimental results on iBims-1 dataset show that the proposed network reduced 1.42 percentage points compared with BS-Net in measuring Directed Depth Error (DDE), and has the proportion of correctly predicted depth pixels reached 81.69%. The above proves that the proposed network has high accuracy in depth prediction.