Intelligent recognition algorithm for hull segment closure surface components based on improved PointNet++

Abstract

ObjectivesThe point cloud data of hull segment closure obtained by a 3D scanner has such advantages as high precision and large data volume, and can accurately reflect the construction status of segment closure. Since the existing PointNet++ network is unable to process large-capacity point cloud data, an algorithm based on improved PointNet++ is proposed to realize the intelligent recognition of components for large-capacity hull segment convergence surface point cloud data. MethodsBased on the hypervoxel growth theory, the hull segment closure point cloud data is segmented and simplified, and a hull segment closure point cloud data set is constructed and used to train a PointNet++ network improved by deep learning theory. ResultsThe convergence results of the network model on the training and testing sets of hull segment closure surface point cloud data tend to be stable, achieving an accuracy rate of 90.012% on the testing set. ConclusionsThe proposed method has good recognition ability and can achieve the intelligent recognition of hull segment closure surface components.

Keywords

• shipbuilding
• artificial intelligence
• hull segment closure surface
• point cloud data
• hypervoxel growth
• pointnet++
• intelligent identification