Research on Unfolding Inner Wall Views of Threaded Pipes

This article proposes a research method for the unfolding of the inner wall view of threaded pipes based on convolutional neural networks (CNNs). First, an industrial endoscope is used to capture the inner wall view of the threaded pipe. Then, a relationship between the lens position and the imaging of the inner wall view is established. An image correction method based on perspective transformation theory is proposed to correct the distortions present in the inner wall view. Finally, an improved image radial unwrapping algorithm is presented, which combines CNNs with image registration. The algorithm is extended based on the VoxelMorph framework and performs radial stretching unwrapping of the images to obtain the planar unfolded view of the threaded pipe's inner wall. Through the experimental analysis, the proposed algorithm is compared with traditional scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) algorithms. The algorithm shows advantages in terms of root-mean-square error (RMSE) and structural similarity index measure (SSIM). The RMSE value is reduced by 0.09, and the SSIM value is improved by 0.24. This method is suitable for the detection of the inner wall of threaded pipes with diameters ranging from 5 to 10 cm, and it demonstrates good unfolding results.