Application of Mask R-CNN Algorithm to Detect Cracks in Concrete Structure

Byongkyu Bae; Yongjin Choi; Kangho Yun; Jaehun Ahn

doi:10.7843/kgs.2024.40.3.33

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2024 Vol.40, Issue 3 Preview Page Next Page

Application of Mask R-CNN Algorithm to Detect Cracks in Concrete Structure 콘크리트 구조체 균열 탐지에 대한 Mask R-CNN 알고리즘 적용성 평가

30 June 2024. pp. 33-39

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Abstract

Inspecting cracks to determine a structure’s condition is crucial for accurate safety diagnosis. However, visual crack inspection methods can be subjective and are dependent on field conditions, thereby resulting in low reliability. To address this issue, this study automates the detection of concrete cracks in image data using ResNet, FPN, and the Mask R-CNN components as the backbone, neck, and head of a convolutional neural network. The performance of the proposed model is analyzed using the intersection over the union (IoU). The experimental dataset contained 1,203 images divided into training (70%), validation (20%), and testing (10%) sets. The model achieved an IoU value of 95.83% for testing, and there were no cases where the crack was not detected. These findings demonstrate that the proposed model realized highly accurate detection of concrete cracks in image data.

Keywords

Convolutional neural network

Crack detection

Deep learning model

Intersection over union

Mask R-CNN

구조물의 상태를 파악하기 위한 균열조사는 정밀안전 진단에 필수적인 검사 항목이다. 그러나 육안으로 이루어지는 균열조사 방식은 현장 상황의 변화에 따라 주관적으로 수행될 수 있다. 이러한 육안검사의 한계를 극복하기 위해 본 연구에서는, ResNet, FPN, Mask R-CNN을 백본(Backbone), 넥(Neck), 헤드(head)로 구성한 합성곱 신경망을 바탕으로, 이미지 데이터에서의 콘크리트 균열 탐지를 자동화하고. 그 성능을 IoU 값을 바탕으로 분석하였다. 해석에 사용된 데이터는 총 1,203개의 이미지 데이터로 구성하였으며, 이 중 70%를 훈련(Training)에, 20%를 검증(Validation)에, 그리고 10%의 데이터를 시험(Testing)에 사용하였다. 시험 결과의 평균 IoU값은 95.83%로 산정되었고, 또한 이미지 내 균열이 전혀 탐지되지 않는 경우는 존재하지 않아, 본 연구에 가정한 모델이 콘크리트의 균열 탐지를 성공적으로 수행하는 것을 확인하였다.

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Information

Publisher :The Korean Geotechnical Society
Publisher(Ko) :한국지반공학회
Journal Title :Journal of the Korean Geotechnical Society
Journal Title(Ko) :한국지반공학회 논문집
Volume : 40
No :3
Pages :33-39
Received Date : 2024-04-09
Revised Date : 2024-05-27
Accepted Date : 2024-05-28
DOI :https://doi.org/10.7843/kgs.2024.40.3.33

Journal of the Korean Geotechnical Society ISSN:1229-2427(Print) 2288-646X(Online) 한국지반공학회 논문집

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