Research on Real-Time Monitoring and Early Warning Method for Bridge Deformation Fusion of Binocular Vision and 3D Reconstruction

This study presents a real-time monitoring and early warning method for bridge deformation based on the integration of binocular vision and 3D reconstruction. To overcome the limitations of traditional measurement techniques, a comprehensive framework is developed, incorporating mathematical modeling, deformation analysis, and predictive assessment. At its core, the Binocular Vision and 3D Reconstruction Fusion Model (BV3RF) combines stereo matching, disparity estimation, and refined 3D point cloud generation. Graphical propagation and probabilistic refinement are introduced to improve the accuracy and robustness of deformation extraction under complex environmental conditions. Using the reconstructed spatial data, a fusion-based monitoring strategy is proposed, which employs temporal modeling, domain-specific constraints, and predictive analytics to track deformation in real time. Structural changes are evaluated by comparing reconstructed states with reference models, while a feedback-driven optimization mechanism enhances system adaptability and stability. Experimental results show that the method improves deformation detection accuracy and enables timely early warnings of potential structural risks. The integration of vision-based sensing, 3D modeling, and predictive monitoring provides an effective solution for dynamic bridge assessment and contributes to the advancement of structural health monitoring technologies.