ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 153-160, 2016
https://doi.org/10.5194/isprs-annals-III-3-153-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
03 Jun 2016
DETECTING AND ANALYZING CORROSION SPOTS ON THE HULL OF LARGE MARINE VESSELS USING COLORED 3D LIDAR POINT CLOUDS
A. K. Aijazi1,2, L. Malaterre1,2, M. L. Tazir1,2, L. Trassoudaine1,2, and P. Checchin1,2 1INSTITUT PASCAL, Université Blaise Pascal, Clermont Université, BP 10448, 63000 Clermont-Ferrand, France
2INSTITUT PASCAL, CNRS, UMR 6602, 63171 Aubière, France
Keywords: 3D LiDAR point clouds, Detection of defects, Ships Abstract. This work presents a new method that automatically detects and analyzes surface defects such as corrosion spots of different shapes and sizes, on large ship hulls. In the proposed method several scans from different positions and viewing angles around the ship are registered together to form a complete 3D point cloud. The R, G, B values associated with each scan, obtained with the help of an integrated camera are converted into HSV space to separate out the illumination invariant color component from the intensity. Using this color component, different surface defects such as corrosion spots of different shapes and sizes are automatically detected, within a selected zone, using two different methods depending upon the level of corrosion/defects. The first method relies on a histogram based distribution whereas the second on adaptive thresholds. The detected corrosion spots are then analyzed and quantified to help better plan and estimate the cost of repair and maintenance. Results are evaluated on real data using different standard evaluation metrics to demonstrate the efficacy as well as the technical strength of the proposed method.
Conference paper (PDF, 3270 KB)


Citation: Aijazi, A. K., Malaterre, L., Tazir, M. L., Trassoudaine, L., and Checchin, P.: DETECTING AND ANALYZING CORROSION SPOTS ON THE HULL OF LARGE MARINE VESSELS USING COLORED 3D LIDAR POINT CLOUDS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 153-160, https://doi.org/10.5194/isprs-annals-III-3-153-2016, 2016.

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