Deep learning algorithm of concrete spalling detection using focal loss and data augmentation

Seungbo Shim; Sang-Il Choi; Suk-Min Kong; Seong-Won Lee

doi:10.9711/KTAJ.2021.23.4.253

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2021 Vol.23, Issue 4 Preview Page Next Page

Research Paper

Deep learning algorithm of concrete spalling detection using focal loss and data augmentation Focal loss와 데이터 증강 기법을 이용한 콘크리트 박락 탐지 심층 신경망 알고리즘

31 July 2021. pp. 253-263

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Abstract

Concrete structures are damaged by aging and external environmental factors. This type of damage is to appear in the form of cracks, to proceed in the form of spalling. Such concrete damage can act as the main cause of reducing the original design bearing capacity of the structure, and negatively affect the stability of the structure. If such damage continues, it may lead to a safety accident in the future, thus proper repair and reinforcement are required. To this end, an accurate and objective condition inspection of the structure must be performed, and for this inspection, a sensor technology capable of detecting damage area is required. For this reason, we propose a deep learning-based image processing algorithm that can detect spalling. To develop this, 298 spalling images were obtained, of which 253 images were used for training, and the remaining 45 images were used for testing. In addition, an improved loss function and data augmentation technique were applied to improve the detection performance. As a result, the detection performance of concrete spalling showed a mean intersection over union of 80.19%. In conclusion, we developed an algorithm to detect concrete spalling through a deep learning-based image processing technique, with an improved loss function and data augmentation technique. This technology is expected to be utilized for accurate inspection and diagnosis of structures in the future.

Keywords

Deep learning

Spalling detection

Semantic segmentation

Data augmentation

Loss function

콘크리트 구조물은 노후화와 외부 환경에 의한 요인으로 훼손된다. 이 같은 훼손은 가장 먼저 균열로 나타나고 향후에는 박락으로도 진행된다. 이러한 콘크리트 손상은 구조물이 갖는 본래의 설계 지지력을 감소시키는 주된 원인으로 작용할 수 있어 구조물의 안정성에 부정적인 영향을 미친다. 이러한 종류의 손상이 지속되면 안전사고로도 이어질 가능성이 있어 적절한 보수와 보강이 필요하다. 이를 위해서는 구조물에 대한 정확하고 객관적인 상태 점검이 이루어져야 하며 손상 영역을 탐지할 수 있는 센서 기술 또한 필요하다. 따라서 본 논문에서는 박락을 탐지할 수 있는 딥러닝 기반의 영상처리 알고리즘을 제안했다. 연구 과정에서 298장의 박락 영상을 확보하였으며, 이 가운데 253장을 학습용으로 사용했고, 나머지 45장을 테스트용으로 사용하였다. 아울러 본 논문에서는 탐지 성능을 향상하기 위해 향상된 손실함수와 데이트 증강 기법을 적용하였다. 그 결과 콘크리트 박락의 탐지 성능이 80.19%의 평균 중첩 정확도로 나타났다. 본 논문에서는 딥러닝 기반의 영상 처리 기법을 통해 콘크리트 박락을 탐지하는 기술을 개발했고, 향상된 손실 함수와 데이터 증강 기법으로 성능을 향상시키는 방법을 제안했다. 이 같은 기술은 향후 구조물의 정확한 점검과 진단에 활용될 것으로 기대된다.

키워드

딥러닝

박락 탐지

의미론적 분할

데이터 증강

손실함수

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Information

Publisher :Korean Tunneling and Underground Space Association
Publisher(Ko) :한국터널지하공간학회
Journal Title :Journal of Korean Tunnelling and Underground Space Association
Journal Title(Ko) :한국터널지하공간학회 논문집
Volume : 23
No :4
Pages :253-263
Received Date : 2021-07-01
Revised Date : 2021-07-14
Accepted Date : 2021-07-14
DOI :https://doi.org/10.9711/KTAJ.2021.23.4.253

Journal of Korean Tunnelling and Underground Space Association ISSN:2233-8292(Print) 2287-4747(Online) 한국터널지하공간학회 논문집

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