A new economical approach for measurement of 3D structural displacement and motion trajectory: Utilizing binocular vision and subpixel enhancement with square feature recognition

Structural health monitoring is essential for ensuring the safety and longevity of engineering structures. Despite significant advancements in vision-based techniques, challenges such as feature recognition and cost-effectiveness have hindered their widespread implementation. This study presents a new economical approach that utilizes binocular vision and subpixel enhancement with square feature recognition to measure three-dimensional (3D) displacements and motion trajectories of structures. The proposed methodology utilizes a binocular camera setup to detect and track square features on structural surfaces, incorporating an automated computer vision algorithm for real-time 3D coordinate computation. The accuracy and reliability of the method are evaluated on custom-designed tubular structures, demonstrating a high precision in measuring 3D displacements and motion trajectories. This solution effectively addresses the challenges associated with high costs and complex configurations inherent in current vision-based techniques, thereby providing an automated and cost-efficient framework for the health monitoring of engineering structures.