Low-Cost Optical Displacement Measurement for SHM Applications Supported by CNN Object Detection

This paper presents a cost-effective approach to structural health monitoring (SHM) based on standard image processing and convolutional neural networks (CNNs) for object detection. The proposed algorithm accurately identifies and tracks geometric measurement motives across image sequences, enabling precise position and two-dimensional displacement determination. Experimental investigations using a minimal implementation with open-source Python (3.12) libraries demonstrate close agreement with reference measurements and low measurement noise. The study also highlights how geometric shape selection, motive arrangement, and preprocessing techniques influence measurement accuracy. This robust, scalable, minimally invasive method offers a low-cost alternative to traditional SHM systems. Its flexibility allows it to be adapted to various infrastructures. Potential future enhancements include strain measurements from multiple motives, multi-plane monitoring, and machine learning–based error correction. These features suggest that the approach is a promising solution for reliable, affordable, and adaptable SHM.