Crack inspection in tunnel structures by fusing information from a 3D light detection and ranging and pan-tilt-zoom camera system
- Authors
- Jeong, Siheon; Kim, Min-Gwan; Kim, Seok-Tae; Oh, Ki-Yong
- Issue Date
- Dec-2023
- Publisher
- Elsevier Ltd
- Keywords
- 3D LiDAR; Autonomous control; Crack inspection; PTZ camera; Structural health monitoring
- Citation
- Structures, v.58, pp 1 - 14
- Pages
- 14
- Indexed
- SCIE
SCOPUS
- Journal Title
- Structures
- Volume
- 58
- Start Page
- 1
- End Page
- 14
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/196298
- DOI
- 10.1016/j.istruc.2023.105420
- ISSN
- 2352-0124
2352-0124
- Abstract
- This study proposes a practical crack inspection method for tunnel structures by fusing measurements from two novel sensors: 3D light detection and ranging (LiDAR) and pan-tilt-zoom (PTZ) camera. The proposed method comprises three phases: prerequisite phase, real-time measurement phase, and inspection phase. First, the 3D LiDAR and the PTZ camera is combined to generate 3D point cloud data (PCD) with RGB data prior to extract the plane contained the crack. Second, the PTZ camera tracked the crack of interest at the pre-registered location. Specifically, several signal processing methods including the Hough transform and random sample consensus are addressed to effectively recognize the crack of interest. Then, the pan and tilt of the camera are controlled to track the crack of interest and record the crack at several magnifications of the zoom automatically because the optimal magnification value of the zoom is unknown in practical field applications. Third, crack propagation rate is estimated by calculating the length of the crack using information on the crack recorded in previous measurements. Cracks in a recorded image are extracted by addressing a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) and pixel-based clustering method. The MSML Mask DCNN constructs distinct feature maps to effectively extract features of cracks and the pixel-based clustering method effectively eliminate cracks not of interest to accurately calculate the crack propagation rate for the crack of interest. Finally, the crack propagation rate was calculated through statistical analysis with several images at different zoom magnifications to secure high accuracy. Field experiments demonstrate that the proposed method is effective to inspect cracks. Systematic analysis on experiments also revealed that all methods deployed in the crack inspection enable ensures high accuracy and robustness in field applications. Consequently, the proposed crack inspection system and method provide a cost-effective and autonomous solution for crack inspection of tunnel structures.
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