Weighted Matrix Decomposition for Small Surface Defect Detection

Abstract

Detecting small defects against a complex surface is highly challenging but crucial to ensure product quality in industry sectors. However, in the detection performance of existing methods, there remains a huge gap in the localization and segmentation of small defects with limited sizes and extremely weak feature representation. To address the above issue, this paper presents a weighted matrix decomposition model (WMD) for small defect detection against a complex surface. Firstly, a weighted matrix is constructed based on texture characteristics of RGB channels in the defect image, which aims to improve contrast between defects and the background. Based on the sparse and low-rank characteristics of small defects, the weighted matrix is then decomposed into low-rank and sparse matrices corresponding to the redundant background and defect areas, respectively. Finally, an automatic threshold segmentation method is used to obtain the optimal threshold and accurately segment the defect areas and their edges in the sparse matrix. The experimental results show that the proposed model outperforms state-of-the-art methods under various quantitative evaluation metrics and has broad industrial application prospects.

EC-YOLO: Effectual Detection Model for Steel Strip Surface Defects Based on YOLO-V5

“…Chen et al [35] incorporated a feature fusion block to effectively amalgamate shallow and deep features from the backbone net, enhancing the identification of minor defects upon steel outsides. Zhong et al [36] proposed the weighted matrix decomposition model (WMD) to identify small flaws on complex surfaces. The sparse moment and low rank are utilized to solve the sparse problem and low rank of small defects on steel surfaces and boost the dissimilarity between the defects and the ground.…”