In dynamic scenarios, visual simultaneous localization and mapping (SLAM) algorithms often incorrectly incorporate dynamic points during camera pose computation, leading to reduced accuracy and robustness. This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability. Firstly, a parallel thread employs the YOLOX object detection model to gather 2D semantic information and compensate for missed detections. Next, an improved K-means++ clustering algorithm clusters bounding box regions, adaptively determining the threshold for extracting dynamic object contours as dynamic points change. This process divides the image into low dynamic, suspicious dynamic, and high dynamic regions. In the tracking thread, the dynamic point removal module assigns dynamic probability weights to the feature points in these regions. Combined with geometric methods, it detects and removes the dynamic points. The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms, providing better pose estimation accuracy and robustness in dynamic environments.