# A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection

arXiv: https://arxiv.org/abs/2112.04021 · HTML: https://mlsanity.com/paper/2112.04021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 33 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network](https://mlsanity.com/paper/2205.10507/) (2022)

## Abstract (arXiv)

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.