[논문]통합 이미지 처리 기술을 이용한 콘크리트 교량 균열 탐지 및 매핑

김병현; 조수진

doi:10.14346/jkosos.2021.36.1.18

[국내논문] 통합 이미지 처리 기술을 이용한 콘크리트 교량 균열 탐지 및 매핑
Crack Inspection and Mapping of Concrete Bridges using Integrated Image Processing Techniques 원문보기

한국안전학회지 = Journal of the Korean Society of Safety, v.36 no.1, 2021년, pp.18 - 25

김병현 (서울시립대학교 토목공학과) , 조수진 (서울시립대학교 토목공학과)

Abstract ▼ AI-Helper

In many developed countries, such as South Korea, efficiently maintaining the aging infrastructures is an important issue. Currently, inspectors visually inspect the infrastructure for maintenance needs, but this method is inefficient due to its high costs, long logistic times, and hazards to the inspectors. Thus, in this paper, a novel crack inspection approach for concrete bridges is proposed using integrated image processing techniques. The proposed approach consists of four steps: (1) training a deep learning model to automatically detect cracks on concrete bridges, (2) acquiring in-situ images using a drone, (3) generating orthomosaic images based on 3D modeling, and (4) detecting cracks on the orthmosaic image using the trained deep learning model. Cascade Mask R-CNN, a state-of-the-art instance segmentation deep learning model, was trained with 3235 crack images that included 2415 hard negative images. We selected the Tancheon overpass, located in Seoul, South Korea, as a testbed for the proposed approach, and we captured images of pier 34-37 and slab 34-36 using a commercial drone. Agisoft Metashape was utilized as a 3D model generation program to generate an orthomosaic of the captured images. We applied the proposed approach to four orthomosaic images that displayed the front, back, left, and right sides of pier 37. Using pixel-level precision referencing visual inspection of the captured images, we evaluated the trained Cascade Mask R-CNN's crack detection performance. At the coping of the front side of pier 37, the model obtained its best precision: 94.34%. It achieved an average precision of 72.93% for the orthomosaics of the four sides of the pier. The test results show that this proposed approach for crack detection can be a suitable alternative to the conventional visual inspection method.

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표/그림 (9)

그림 Fig. 1. Automated crack detection and mapping framework using integrated image processing.
그림 Fig. 2. Overall structure of cascade mask R-CNN.
그림 Fig. 3. Examples of hard negative training images (Left: tree branches, right: construction joint).
표 Table 1. Number of images captured by drone at Tancheon overpass (W.D. is short for working distance)
그림 Fig. 4. Overall procedure for bridge orthomosaic generation.
그림 Fig. 5. Orthomosiac of (a) slab 34 and (b) Pier 35.
그림 Fig. 6. Crack detection result visualization.
그림 Fig. 7. Crack detection evaluation example: (a) Original image of concrete crack and (b) pixel-level precision calculation (red area : true positive, green area: false positve).
표 Table 2. Pixel-level precision of the trained cascade mask R-CNN (Compared with inspection report)

참고문헌 (22)

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[국내논문] 통합 이미지 처리 기술을 이용한 콘크리트 교량 균열 탐지 및 매핑
Crack Inspection and Mapping of Concrete Bridges using Integrated Image Processing Techniques 원문보기

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[국내논문] 통합 이미지 처리 기술을 이용한 콘크리트 교량 균열 탐지 및 매핑 Crack Inspection and Mapping of Concrete Bridges using Integrated Image Processing Techniques 원문보기

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Crack Inspection and Mapping of Concrete Bridges using Integrated Image Processing Techniques 원문보기

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