Automated Detection of Rust Defects from 3D Point Cloud Data Through Machine Learning

We introduce a method for automatic corrosion detection based on the application of machine learning techniques to 3D point cloud data generated by a LIDAR sensor. In our approach a point is assigned one of the considered class labels (healthy, stain, weld or rust) by processing its feature vector with a cascade of three binary classifiers. The effectiveness of the proposed system is demonstrated through a case study on three different bulkheads in the hold of a merchant ship. The experimental results show that the corrosion detection rate is improved by combining colour and local geometry features.