  In this paper, we present a Robust Completed Local Binary Pattern (RCLBP)
framework for a surface defect detection task. Our approach uses a combination
of Non-Local (NL) means filter with wavelet thresholding and Completed Local
Binary Pattern (CLBP) to extract robust features which are fed into classifiers
for surface defects detection. This paper combines three components: A
denoising technique based on Non-Local (NL) means filter with wavelet
thresholding is established to denoise the noisy image while preserving the
textures and edges. Second, discriminative features are extracted using the
CLBP technique. Finally, the discriminative features are fed into the
classifiers to build the detection model and evaluate the performance of the
proposed framework. The performance of the defect detection models are
evaluated using a real-world steel surface defect database from Northeastern
University (NEU). Experimental results demonstrate that the proposed approach
RCLBP is noise robust and can be applied for surface defect detection under
varying conditions of intra-class and inter-class changes and with illumination
changes.
