Evaluation of feature- and pixel-based methods for deflection measurements in temporary structure monitoring

Abstract

Recent forensic studies have revealed quite a few construction site catastrophes are associated with failure of temporary structures (e.g., collapse of formworks, scaffoldings, etc.). This is particularly crucial for those located in dense urban areas where the failure of a temporary structure not only impacts the site itself but may also damage adjacent structures and cause injuries and casualties of passers-by. In this paper, the work of evaluating the optimum feature detection and matching algorithm is reported, which is the key in realizing a real-time visual sensing-based surveillance method in order to monitor the integrity and safety of on-site temporary structures. A series of experiments are designed and conducted to test three algorithms: population-based intelligent digital image correlation (DIC), David Lowe’s Scale-invariant feature transform, and Hessian matrix-based speeded-up robust features. In these experiments, synthetic images through 2D geometrical translation, rotation, deformation, and illumination changes are generated to provide the sample data and ground truths. As the experiment output, the accuracy and efficiency of these algorithms are measured and compared with each other. Analyses including feasibility of interest point localization with specific regions and the error estimation with respect to real-world distances are conducted. The results show that the DIC algorithm holds the most promise to be implemented in structural monitoring, but several challenges still remain, which call for efforts to further improvement of the technique.