Multi-Resolution and Noise-Resistant Surface Defect Detection Approach Using New Version of Local Binary Patterns - Shervan Fekri-Ershad; Farshad Tajeripour (Applied Artificial Intelligence, 2017)
Visual quality inspection systems play an important role in many industrial applications. In this respect, surface defect detection is one of the problems that have received much attention by image processing scientists. Until now, different methods have been proposed based on texture analysis. An operation that provides discriminate features for texture analysis is local binary patterns (LBP). LBP was first introduced for gray-level images that makes it useless for colorful samples. Sensitivity to noise is another limitation of LBP. In this article, a new noise-resistant and multi-resolution version of LBP is used that extracts color and texture features jointly. Then, a robust algorithm is proposed for detecting abnormalities in surfaces. It includes two steps. First, new version of LBP 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 windows and basic vector, a threshold is computed. In test phase, defect parts are detected on test samples using the tuned threshold. High detection rate, low computational complexity, low noise sensitivity, and rotation invariant are some advantages of our proposed approach.