Self-supervised monocular depth estimation methods trained on sequences of monocular images have received considerable attention in recent years by using the photometric consistency loss between adjacent frames instead of depth labels as the supervisory signal for network training. The photometric consistency constraint follows the static world assumption， but the moving objects in the monocular image sequence violate this assumption， which affects the camera pose estimation accuracy and the calculation accuracy of the photometric loss function during the self-supervised training process. By detecting and removing the moving target area， the camera pose decoupled from the target motion can be obtained， and the in?uence of the moving target area on the calculation accuracy of the photometric loss can be discarded. To this end， this paper proposes a self-supervised monocular depth estimation network based on semantic assistance and depth temporal consistency constraints. First， an offline instance segmentation network is used to detect dynamic category objects that may violate the static world assumption， and the corresponding region input pose network is removed to obtain a camera pose decoupled from object motion. Secondly， based on semantic consistency and photometric consistency constraints， the motion status of dynamic category targets is detected so that the photometric loss in the moving area does not affect the iterative update of network parameters.Finally， depth temporal consistency constraints are imposed in non-motion areas， and the estimated depth value of the current frame is explicitly aligned with the projected depth value of adjacent frames to further refine the depth prediction results. Experiments on the KITTI， DDAD and KITTI Odometry datasets verify that the proposed method has better performance than previous self-supervised monocular depth estimation methods.