Underwater-Sonar-Image-Based 3D Point Cloud Reconstruction for High Data Utilization and Object Classification Using a Neural Network
Minsung Sung, Jason Kim, Hyeonwoo Cho, Meungsuk Lee, Son‐Cheol Yu
Abstract
This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a neural network can predict the class given the generated point cloud. By reconstructing the 3D shape of the object, the proposed method can classify the object accurately through a straightforward training process. We verified the proposed method by performing simulations and field experiments.
Topics & Concepts
SonarUnderwaterPoint cloudObject (grammar)Artificial intelligenceComputer visionComputer scienceProcess (computing)Point (geometry)Artificial neural networkObject detectionCognitive neuroscience of visual object recognitionPattern recognition (psychology)GeologyMathematicsGeometryOceanographyOperating systemRobotics and Sensor-Based LocalizationRemote Sensing and LiDAR ApplicationsUnderwater Acoustics Research