An End-To-End Model for Pipe Crack Three-Dimensional Visualization Based on a Cascade Neural Network

Abstract

With the continuous progress of machine vision technology, crack detection in pipelines has been greatly improved. For crack detection in deep holes, inner tubes, and other environments, it is not only necessary to detect the existence of cracks, but also to collect important information regarding the crack detection direction for further analysis. Because shooting with a frontal field of view causes the real side wall images to produce certain distortions, the detection and calibration of cracks requires a certain amount of professional technology and time. It usually takes a long time to collect the image to eliminate the distortion, and then to identify the crack and mark the direction according to the data line. Therefore, a simple and efficient end-to-end neural network model for crack recognition and three-dimensional visualization are proposed by using a cascade network and simple recognition technology in conjunction with inertial navigation equipment. In addition, we screen the crack data via pixel calibration and eliminate the ambiguous data to make the visualization more accurate. Experiments in pipelines and burrows show that the accuracy, performance, and efficiency of the proposed method reached a high level.

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces

“…In [ 15 ], the authors utilized an active stereo omnidirectional vision sensor to obtain panoramic images of the pipeline’s internal surface. Fang et al [ 16 ] proposed an interesting method that restores the images of pipe walls using images captured from the front view. They addressed the radial distortion issue of the front-view images by employing nonlinear fitting features of a neural network and homemade feature stickers.…”