Pipeline Damage Depth Estimation and 3D Reconstruction Based on Boosting-Monodepth Algorithm

Abstract:

Urban underground pipelines are the blood channels of the city. However， with the large number of drainage pipelines put into operation and their service life increasing， a series of hidden dangers of pipeline diseases have arisen， such as the overall structural deformation of the pipeline， internal surface cracking， and the insertion of foreign matters in the pipeline. Traditional disease image video capture， detection， and later disease classification and selection are conducted from a two-dimensional（2D） perspective， lacking consideration of three-dimensional spatial information （depth）. Aimed at the above three diseases， an experiment is conducted from generating depth maps and reconstructing three-dimensional（3D） pipeline diseases from 2D depth maps. A dual depth estimation method based on boosting-monodepth is proposed to improve the effect of depth maps， and finally a depth map with continuous and consistent images and clear outlines is generated. In terms of performance evaluation， Abs-Rel， RMSE， SqRel， ORD， D3R and other general indexes are compared with those of the traditional algorithm， and the results show that the boosting-monodepth is reduced by 30% at RMSE and increased by 18% at a threshold of δ< 1.25. Afterwards， the 3D point cloud of pipeline disease is reconstructed based on the depth map obtained， and 3D visualization is performed on CloudCompare. Finally， the disease depth is calculated using the random sampling consistent algorithm and compared with the real measured data to prove its effectiveness and accuracy.