ROBUST DETECTION OF SURFACE ANOMALY USING LIDAR POINT CLOUD WITH INTENSITY

Abstract. We have developed an automatic detection method for metallic corrosion in facilities by using a LiDAR point cloud. While visual inspections for monitoring facilities are widely conducted, the inspection result depends on human skill, and there is currently a shortage of inspectors. While automatic detection methods using an RGB image have been developed, such methods cannot be applied to inspections at night. Therefore, we propose a robust detection method that utilizes both 3D shapes and intensities in a LiDAR point cloud instead of RGB information. The proposed method segments the point cloud into a basic building material by using the 3D shape and then recognizes a point cloud with an abnormal intensity in each material as the corrosion area. We demonstrate through experiments that the proposed method can robustly detect corrosion spots in aging facilities during detection conducted both during the day and at night.