Aiming at the problem that dynamic objects in indoor scenes affect the accuracy of camera pose estimation seriously， a Simultaneous Localization And Mapping （SLAM） system for indoor dynamic scenes based on target detection was proposed. After the camera capturing an image， the YOLOv4 target detection network was used to detect dynamic objects in the environment and generate the mask area of the corresponding bounding box at first. Then， the ORB feature points in the image were extracted， and the feature points inside the mask area were removed. At the same time， the GMS （Grid-based Motion Statistics） algorithm was combined to further eliminate mismatches， and only the remaining static feature points were used to estimate the camera pose. Finally， the construction of a static dense point cloud map and an octomap filtering out dynamic objects was completed. Results of multiple comparison tests on TUM RGB-D public dataset show that compared to ORB-SLAM2 system， GCNv2_SLAM system and YOLOv4+ORB-SLAM2 system， the proposed system has the Absolute Trajectory Error （ATE） and Relative Pose Error （RPE） significantly reduced， indicating that this system can improve the accuracy of camera pose estimation in indoor dynamic environments significantly.