Work

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

Year: 2025

Type: article

Abstract: 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 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.

Source: Applied Sciences

DOI: 10.3390/app152412938

Authors: Dennis Messerer, Martin Weisbrich, Klaus Holschemacher

Institution: Leipzig University of Applied Sciences

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896