A sparse-representation-based robust inspection system for hidden defects classification in casting components

In this paper, a robust sparse-representation-based inspection system for the detection and classification of casting hidden defects in radiographs is presented. Four common types of casting defects including cracks, blow holes, shrinkage porosities and shrinkage cavities are considered in our system. In the framework, a Gray Arranging Pairs (GAP) based segmentation method is implemented firstly. This method can deal with the case of casting that has complex structure well and is robust against non-uniform illumination variations and noise. Second, a Randomly Distributed Triangle (RDT) feature is extracted to represent the geometric characteristic of each defect. This feature uses random triangle samplings which are formed from the defect shape to produce a continuous probability distribution. It is simple and can discriminate defects correctly despite of rotation, scale and noise. Third, a Sparse Representation-based Classification (SRC) is trained to classify each of the input defect into one of the classes. The performance of the proposed method is shown in the experiment by comparing with the SVM classifier.