Improvement of ALB data analysis method using machine learning for rescuer search in water rescue

In recent years, water-related accidents caused by torrential rain have been occurring frequently. Visual search for persons requiring rescue is challenging from coast or riverbank. Due to water currents and underwater topography, search from boat is also difficult. This research aims to develop a safe, wide area and accurate target search method using point cloud data from drone. The authors focused on a LiDAR system called Airborne Laser Bathymetry (ALB) which is specialized for underwater observation. A green laser ALB, In particular, has capability to obtain underwater topography data because it is equipped with not only near-infrared laser used in conventional land surveying but also green visible laser for observing in relatively shallow water. The purpose of this study is to make it possible to identify the water surface, underwater topography, and underwater floating objects such as algae from green laser ALB point cloud data using machine learning methods. For machine learning, I use Pointnet⁺⁺, a network effective for point cloud processing, and SVM (Support Vector Machine), specialized for two class classification. The Pointnet⁺⁺ addresses the limitations of the previously used Pointnet by sampling local features based on point cloud distance and density for learning. In proposed method, Pointnet⁺⁺ is used to input three-dimensional coordinates X, Y and Z and extract three classes: water surface, underwater topography, and floating objects. Then, by inputting the Z-axis coordinate data and backscatter data (Intensity) into the SVM, it becomes possible to detect persons requiring rescue from among the floating objects.