A Robust Approach for Surface Defect Detection Based on one Dimensional Local Binary Patterns

Defect detection is one of the problems in image processing and many different methods based on texture analysis have been proposed. The two dimensional local binary pattern approach provides discriminate features for texture analysis. In this paper for the first time, a method is proposed for detecting abnormalities in surface textures based on single dimensional local binary patterns. The proposed approach includes two steps. Firstly, in training step, single dimensional local binary patterns is applied on full defect-less surface images and the basic feature vector is calculated. Then, by image windowing and computing the non-similarity amount between these windows and basic vector, a threshold is computed for defect-less surfaces. Finally, in testing step, by using the defect-less threshold the defects are detected on test images. High detection rate, and low computational complexity are advantages of the proposed approach. The proposed approach is fully automatic and all of the necessary parameters can be tuned.