| dc.description.abstract | The change and displacements of slopes has always been an important
factor in determining whether they will collapse. Digital photogrammetric
methods, which are characterized by high safety, low installation cost, and
high mobility, can effectively ensure the stability of slopes by continuously
capturing and observing them over a long period of time. This study aims
to combine optical imaging technology, infrared thermography, and
machine learning methods for change detection in dual-temporal images.
By utilizing a micro single board computer, specifically the Raspberry Pi,
in combination with two camera modules, images are captured at the same
location but at different times. Through machine learning, the computer
can predict the locations of changes in the bi-temporal optical image set.
Numerical processing is used to calculate temperature changes in the bi-
temporal infrared thermographic image set. Initially, indoor model
experiments were conducted using a self-collected set of images to train
the machine learning model and analyze the feasibility and effectiveness
of the change detection model. Subsequently, the monitoring system was
deployed outdoors, setting up a monitoring station near existing slopes.
The monitoring results demonstrated that the proposed method in this study
can effectively perform long-term monitoring operations in outdoor
environments. The change detection score of mean F1-score and mean IoU
in the parking lot of National Central University can reach 0.9359 and
0.8850 respectively, showcasing good performance in the Nanya Village,
Ruifang District, New Taipei City. | en_US |