Study on Accuracy Improvement of Slope Failure Region Detection Using Mask R-CNN with Augmentation Method.

We proposed an automatic detection method of slope failure regions using a semantic segmentation method called Mask R-CNN based on a deep learning algorithm to improve the efficiency of damage assessment in the event of slope failure disaster. There is limited research on detecting landslides by deep learning, and the lack of training data is an important issue to be resolved, as aerial photographs are not taken with sufficient frequency during a disaster. This study attempts to use CutMix-based augmentation to improve detection accuracy.

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results.

It is necessary to assess damage properly for the safe use of a structure and for the development of an appropriate maintenance strategy. Although many efforts have been made to measure the vibration of a structure to determine the degree of damage, the accuracy of evaluation is not high enough, so it is difficult to say that a damage evaluation based on vibrations in a structure has not been put to practical use. In this study, we propose a method to evaluate damage by measuring the acceleration of a structure at multiple points and interpreting the results with a Random Forest, which is a kind of supervised machine learning.