A post-processing algorithm for automatic recognition of tunnel crack diseases based on segmentation masks
With the construction of transportation networks,the number of completed tunnels and the increasing service life of tunnels have brought great challenges to the safe operation of tunnels.Rapid detection of tunnel lining cracks and accurate ex-traction of crack length and width characteristics is an important guarantee for achieving efficient maintenance and safe opera-tion of tunnel.This article proposes an efficient and accurate post-processing algorithm for tunnel crack diseases,based on the prediction segmentation mask of DeepLabV3+semantic segmentation model.The connected domain discrimination refinement algorithm and endpoint clustering instance differentiation algorithm are used to process the mask fracture situation,achieving accurate extraction of tunnel crack skeleton and instance differentiation.Finally,the length calculation and grayscale difference value width classification algorithm are used to calculate the crack length and width characteristics.The accuracy of length and width calculation is 92.2％and 86.3％,respectively.
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