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Development of Diagnosis Application for Rail Surface Damage using Image Analysis Techniques

이미지 분석기법을 이용한 레일표면손상 진단애플리케이션 개발

  • Received : 2024.01.02
  • Accepted : 2024.01.31
  • Published : 2024.03.31

Abstract

The recently enacted detailed guidelines on the performance evaluation of track facilities presented the necessary requirements regarding the evaluation procedures and implementation methods of track performance evaluation. However, the grade of rail surface damage is determined by external inspection (visual inspection), and there is no choice but to rely only on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we attempted to develop a diagnostic application that can diagnose rail internal defects using rail surface damage. In the field investigation, rail surface damage was investigated and patterns were analyzed. Additionally, in the indoor test, SEM testing was used to construct image data of rail internal damage, and crack length, depth, and angle were quantified. In this study, a deep learning model (Fast R-CNN) using image data constructed from field surveys and indoor tests was applied to the application. A rail surface damage diagnosis application (App) using a deep learning model that can be used on smart devices was developed. We developed a smart diagnosis system for rail surface damage that can be used in future track diagnosis and performance evaluation work.

최근 제정된 궤도시설의 성능평가에 관한 세부지침에서 궤도성능평가의 평가절차 및 실시방법 등에 관한 필요사항을 제시하였다. 그러나 외관조사(육안조사)에 의해 레일표면손상의 등급이 결정되며, 점검자의 주관적인 판단으로 정성적인 평가에만 의존할 수밖에 없는 실정이다. 따라서 본 연구에서는 레일표면손상을 이용하여 레일내부결함까지 진단할 수 있는 진단애플리케이션을 개발하고자 하였다. 현장조사에서는 레일표면손상을 조사하고 패턴을 분석하였다. 또한 실내시험에서는 레일내부손상 이미지 데이터를 구축하기 위하여 SEM 시험을 이용하였으며, 균열 길이, 깊이 및 각도를 정량화하였다. 본 연구에서는 현장조사와 실내시험에서 구축한 이미지 데이터를 적용한 딥러닝 모델(Fast R-CNN)을 애플리케이션에 적용하였다, 스마트기기에서 사용이 가능한 딥러닝 모델을 이용한 레일표면손상 진단 애플리케이션(App)을 개발하여 향후 궤도진단 및 성능평가 업무에 활용 가능한 레일표면손상 스마트 진단시스템을 개발하였다.

Keywords

Acknowledgement

이 논문은 2023년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No. RS-2023-00233470, 인공신경망 이미지 분석을 이용한 레일표면손상 진단시스템 개발)

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