Research on crack identification of tunnel lining based on image processing

Xiang, Yang; Qiao, Jing

doi:10.1109/icceai55464.2022.00073

Cited by 1 publication

(1 citation statement)

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“…There are many noises in tunnel image, mainly Gaussian noise and salt and pepper noise. Bilateral filtering has the function of double filtering, which can better preserve the edge on the basis of filtering Gaussian noise [6] . Median filtering can effectively remove salt and pepper noise.…”

Section: Tunnel Crack Image Filteringmentioning

confidence: 99%

Subway tunnel crack detection method based on image processing

fu,

wu,

zhao

2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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The crack of subway tunnel seriously affects the service life of the tunnel and endangers the safety of subway traffic. The efficiency of manual detection is low, this paper proposes a tunnel crack detection method based on image processing. The images of tunnel cracks collected inside the subway tunnel suffer from problems such as uneven lighting, low contrast, and severe noise, which affect the recognition of cracks. To address these issues, a multi-scale Retinex algorithm combined with linear stretching is firstly used to preprocess the images, effectively balancing the lighting. Secondly, an improved adaptive median filter algorithm is used to filter out image noise while effectively preserving the crack edges. Thirdly, the Scharr operator combined with the Otsu method is used to segment the filtered image, effectively separating the crack area. Fourth, the crack binary image without noise is obtained using connected domain filtering and morphological processing. Finally, the crack length, average width, maximum width, and area are calculated using the crack skeleton image. The research results show that the proposed algorithm can effectively identify crack areas, demonstrating its effectiveness.

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Section: Tunnel Crack Image Filteringmentioning

confidence: 99%