Vision-based structural health monitoring offers a promising alternative to conventional wired sensing systems; however, its adoption is often limited by high hardware costs and computational constraints at sensing nodes. This study presents the design and laboratory validation of a low-cost vision-based system for displacement and strain monitoring using a centralized processing architecture. The proposed system separates image acquisition from computation, where an ESP32-CAM module serves as a lightweight edge node for grayscale image capture and wireless transmission, while computational tasks including displacement tracking, subpixel localization, scale calibration, and strain estimation are performed on a centralized unit. This enables low-cost deployment at USD 60 per node with low power consumption at 1 W. System performance was evaluated through controlled experiments, including a 24 h zero-drift test and quasi-static displacement tests up to 15 μm. Validation against a Linear Variable Differential Transformer (LVDT) shows close agreement, with an absolute error of 2.63 µε and drift within ±2 μm. The system achieves an effective strain range of ±35,000 με. These results demonstrate the potential of low-cost centralized vision-based systems, demonstrating strong potential for practical deployment in structural health monitoring applications.
Anim et al. (Fri,) studied this question.