Supervoxel Based Object Detection and Seafloor Segmentation Using Novel 3d Side-Scan Sonar

Abstract

Object detection and seafloor segmentation for conventional 2D side-scan sonar imagery is a well-investigated problem. However, due to recent advances in sensing technology, the side-scan sonar now produces a true 3D point cloud representation of the seafloor embedded with echo intensity. This creates a need to develop algorithms to process the incoming 3D data for applications such as object detection and segmentation, and an opportunity to leverage advances in 3D point cloud processing developed for terrestrial applications using optical sensors (e.g. LiDAR). A bottleneck in deploying 3D side-scan sonar sensors for online applications is attributed to the complexity in handling large amounts of data which requires higher memory for storing and processing data on embedded computers. The present research aims to improve data processing capabilities on-board autonomous underwater vehicles (AUVs). A supervoxel-based framework for over-segmentation and object detection is proposed which reduces a dense point cloud into clusters of similar points in a neighborhood. Supervoxels extracted from the point cloud are then described using feature vectors which are computed using geometry, echo intensity and depth attributes of the constituent points. Unsupervised density based clustering is applied on the feature space to detect objects which appear as outliers.