Dynamic Object Mapping Generation Method of Digital Twin Construction Scene

The construction environment is a highly dynamic and complex system, presenting challenges for accurately identifying and managing dynamic resources in digital twin-based scenes. This study aims to address the problem of object coordinate distortion caused by camera image deformation, which often reduces the fidelity of dynamic object mapping in digital construction monitoring. A novel dynamic object mapping generation method is proposed to enhance precision and synchronization of dynamic objects within a digital twin environment. The approach integrates internal and external camera parameters, including spatial position, field of view (FOV), and camera pose, into BIM using Dynamo, thereby creating a virtual camera aligned with the physical one. The YOLOv11 algorithm is employed to recognize dynamic objects in real-time camera footage, and corresponding object families are generated in the BIM model. Using perspective projection combined with a linear regression model, the system computes and updates accurate coordinate positions of the dynamic objects, which are then fed back into the camera view to achieve real-time mapping. Experimental validation demonstrates that the proposed method significantly reduces mapping errors induced by lens distortion and provides accurate spatial data, supporting improved dynamic resource perception and intelligent management in digital twin construction environments.