A Study on Automatic Classification of Characterized Ground Regions on Slopes by a Deep Learning based Image Segmentation

Kyu Beom Lee; Hyu-Soung Shin; Seung Hyeon Kim; Dae Mok Ha; Isu Choi

doi:10.7474/TUS.2019.29.6.508

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2019 Vol.29, Issue 6 Preview Page Next Page

Original Article

A Study on Automatic Classification of Characterized Ground Regions on Slopes by a Deep Learning based Image Segmentation 딥러닝 영상처리를 통한 비탈면의 지반 특성화 영역 자동 분류에 관한 연구

31 December 2019. pp. 508-522

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Abstract

Because of the slope failure, not only property damage but also human damage can occur, slope stability analysis should be conducted to predict and reinforce of the slope. This paper, defines the ground areas that can be characterized in terms of slope failure such as Rockmass jointset, Rockmass fault, Soil, Leakage water and Crush zone in sloped images. As a result, it was shown that the deep learning instance segmentation network can be used to recognize and automatically segment the precise shape of the ground region with different characteristics shown in the image. It showed the possibility of supporting the slope mapping work and automatically calculating the ground characteristics information of slopes necessary for decision making such as slope reinforcement.

Keywords

Instance segmentation

Deep learning

Slope failure image

Ground characteristics of Slope

비탈면의 붕괴로 인해 재산피해뿐만 아니라 인명피해 또한 발생할 수 있으므로 안정성 평가를 통해 비탈면의 붕괴여부 예측 및 보강을 진행해야 한다. 본 논문은 비탈면 영상에서 암반 절리군, 암반 단층, 토양, 비탈면 누수영역 등 비탈면 붕괴와 관련하여 특성화 시킬 수 있는 지반 영역들을 정의하고 이를 딥러닝 기법을 통해 자동으로 분류해 낼 수 있는 방법에 대해 고찰하였다. 이에 따라 딥러닝 객체 영역분할(Instance segmentation) 네트워크를 활용하여 영상에 보여지는 다른 특성을 갖는 지반영역의 정확한 형상을 인식하고 자동 분할 할 수 있음을 보였으며, 향후 비탈면 안정성 평가를 위해 시행되는 비탈면 매핑 작업을 지원하고, 비탈면 보강 대책 등 의사결정에 필요한 비탈면의 지반특성 정보를 자동으로 산출할 수 있는 가능성을 보였다.

키워드

Instance segmentation

딥러닝

비탈면 붕괴영상

비탈면 지반특성

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Information

Publisher :Korean Society for Rock Mechanics and Rock Engineering
Publisher(Ko) :한국암반공학회
Journal Title :Tunnel and Underground Space
Journal Title(Ko) :터널과 지하공간
Volume : 29
No :6
Pages :508-522
Received Date : 2019-12-09
Revised Date : 2019-12-20
Accepted Date : 2019-12-23
DOI :https://doi.org/10.7474/TUS.2019.29.6.508

Tunnel and Underground Space ISSN:1225-1275(Print) 2287-1748(Online) 터널과 지하공간

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