Automated Visual Inspection of Flat Surface Products using Feature Fusion

Defect detection on industrial flat surface products like textiles, steel slabs, metal plates, plastic films, painted car body, parquet slabs and paper is a necessary requirement for quality control and satisfaction of consumers. This paper presents a system for feature extraction and fusion in order to enhance the performance of the defect detection process. A multi-feature fusion technique based on PCA is presented. Features based on Co-occurrence matrix, Laws filters, moment invariants, moment of inertia and standard deviation of gray levels are integrated into a one dimensional feature vector which uniquely differentiates the normal from abnormal textures of a flat surface product. PCA has been used to reduce the feature set into eight significant features. A learning vector quantization neural network is used for classification of product surface image blocks as normal or abnormal. Detection accuracies using the individual feature sets and the fused features are compared. The results obtained from multi-feature fusion outperformed those obtained from the individual feature sets and indicate that the multi-feature fusion improves the accuracy of detection and speeds up the process. Empirical results show the high accuracy of the presented approach (97.96%).