[논문]무인이동체와 딥러닝 기반 이미지 분석 기술을 활용한 철도교량 자동 손상 분석 방법 연구

나용현; 박미연

doi:10.15683/kosdi.2021.9.30.556

무인이동체와 딥러닝 기반 이미지 분석 기술을 활용한 철도교량 자동 손상 분석 방법 연구
A Study of Railway Bridge Automatic Damage Analysis Method Using Unmanned Aerial Vehicle and Deep Learning-based Image Analysis Technology 원문보기

한국재난정보학회논문집 = Journal of the Society of Disaster Information, v.17 no.3 = no.53, 2021년, pp.556 - 567

나용현 (SH Tech & Policy Institute Co.) , 박미연 (SH Tech & Policy Institute Co.)

초록
AI-Helper

연구목적: 본 연구에서는 무인이동체를 활용한 철도교량의 외관조사 점검을 보다 효율적이고 신뢰성 있게 점검을 위하여 무인이동체를 통해 촬영된 이미지를 바탕으로 다양한 방식의 딥러닝 기반 자동 손상 분석기술을 검토하였다. 연구방법: 취득된 이미지를 바탕으로 손상항목을 정의하고 학습데이터로 추출하여 딥러닝 분석 모델을 생성하였다. 그리고 철도교량의 외관 손상 중 균열, 콘크리트 박리·박락, 누수, 철근노출에 대한 손상 이미지를 학습한 모델을 적용하여 자동 손상 분석 결과로 테스트하였다. 연구결과: 분석 결과 평균 95%이상 검측 재현율을 도출하는 분석 기법을 검토할 수 있었다. 이와 같은 분석 기술은 기존 육안점검 결과 대비 보다 객관적이고 정밀한 손상 검측이 가능하다. 결론: 본 연구를 통해 개발된 기술을 통해 철도 유지관리 분야에서 무인이동체를 활용한 정기점검 시 자동손상분석을 통한 객관적인 결과도출과 기존 대비 소요시간, 비용저감이 가능할 것으로 기대된다.

Abstract ▼ AI-Helper

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

주제어

표/그림 (12)

표 Table 1. Railroad bridge damage items and type
표 Table 2. Specifications of UAV and camera devices
그림 Fig. 1. Deep learning image analysis process
표 Table 3. Acquired image data for each damaged
그림 Fig. 2. Concepts by deep learning algorithms by detection type of light Head-RCNN
그림 Fig. 3. Concepts by deep learning algorithms by segmentation type
그림 Fig. 4. Deep learning image analysis recall verification
그림 Fig. 5. Process of detecting recall rate evaluation
표 Table 4. Definition of confusion matrix
표 Table 5. Analysis results for each damage
그림 Fig. 6. Analysis result graph by deep learning analysis method.
그림 Fig. 7. Automatic damage analysis results in railway bridge testbed

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