계층적 학습 기반 다중 콘크리트 손상에 대한 의미론적 분할
Semantic Segmentation for Multiple Concrete Damage Based on Hierarchical Learning
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초록
구조물의 공용연수가 증가함에 따라 각종 성능 저하가 발생한다. 특히 국내 인프라 구조물은 대부분 경제가 성장하는 시기에 집중적으로 건설되었기 때문에 노후 인프라 비율 급증이 최근 주요 이슈가 되고 있다. 인프라의 노후화는 자칫 안전사고로 이어질 수 있으며 인명피해까지 유발할 수 있다. 이러한 문제를 사전에 예방하기 위하여 주기적이고 정확한 점검 및 유지관리가 필수적이다. 이 같은 이유로 최근 컴퓨터 비전과 딥러닝을 활용하여 다양한 손상을 탐지하는 연구에 대한 수요가 원격점검 혹은 점검자동화 분야에서 증가하고 있다. 따라서 본 논문에서는 콘크리트 손상의 종류를 세 가지로 구분하여 이를 탐지할 수 있는 신경망 구조를 제안했다. 특히 계층적 학습 기법을 통해 보다 정확하게 다양한 손상을 탐지할 수 있는 신경망을 개발하였다. 이 신경망은 2,026장의 손상 영상으로 학습되었고, 508장의 손상 영상으로 실험하였다. 그 결과 67.04%의 평균 중첩 정확도와 52.65%의 F1 점수를 갖는 알고리즘을 완성하였다. 이 같은 손상 탐지 알고리즘은 향후 구조물의 정확한 상태 진단에 활용될 수 있으리라 기대한다.
Abstract
The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built duringthe period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safetyaccidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for researchto detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomousinspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular,the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04%and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the nearfuture
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