AR-DIC: A Novel Automatic Optical Strategy for Displacement Measurement of the Curved-in-Place Pipe Under Dynamic Fracturing

Curved-in-place pipe (CIPP) technology is widely used in urban underground pipeline repair nowadays. However, the lack of automatic global displacement acquisition method makes it still quite challenging to effectively obtain the detailed displacement characteristics of the CIPP pipes under dynamic loads. Herein, we propose a cascade strategy named automatic speckle region extraction digital image correlation (AR-DIC) to achieve the dynamic, efficient, and accurate displacement estimation of the CIPP pipelines, so as to determine the variation characters of the CIPP pipelines under dynamic loads. In the first stage, an improved automatic DeepLabv3+ neural network (IA-DeepLabv3+) is meticulously designed to extract the region of interest (ROI) of the CIPP pipeline speckle automatically. A dataset containing the cluttered background and the real experimental speckle is also generated to train the model parameters. Note that this is the first time an automated ROI extraction method has been proposed in CIPP, even in the digital image correlation (DIC) field. In the second stage, the DIC based on scale-invariant feature transform (SIFT-DIC) algorithm is adopted to calculate the extracted ROI and get the displacement field. Finally, the proposed method is tested on both the simulated and experimental datasets. The experimental results confirm that our method can not only extract the speckle ROI region accurately, but also achieve high-precision calculation of the displacement field.