Corrosion segmentation method of concrete drainage pipes based on point transformer

Abstract

Deep learning based Closed Circuit Television (CCTV) image detection has achieved remarkable accuracy in identifying and segmenting concrete pipeline damages. However, the lack of in-depth information and requirement for large amounts of training data are two major drawbacks. Therefore, this study proposes a point cloud data segmentation method that incorporates depth information to address the issue of insufficient training data for depth models, enabling efficient and accurate segmentation. Firstly, 3D simulation technology is employed to construct a simulation dataset. Secondly, an optimized point transformation model is established to identify and segment the point cloud data. The proposed method achieved a test accuracy of 94.36 % by optimizing the network structure and the training strategy. The Mean Intersection over Union (MIoU) increased by 3.62 % and 10.3 %, respectively, compared with the classic PointNet++ and before adding simulation data, reaching 86.31 %. These results demonstrate that the proposed method exhibits high precision defect detection capability on three-dimensional point clouds of drainage pipelines.