A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection
This work addresses surface defect detection for industrial quality control, but it is incremental as it combines existing techniques like Non-Local means filtering and CLBP.
The paper tackled surface defect detection by proposing a Robust Completed Local Binary Pattern (RCLBP) framework, which achieved noise robustness and applicability under varying conditions, as demonstrated on a real-world steel surface defect database.
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.