Zhong Zhiyan, Wang Hongxin, Xiang Dan
School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou 510006, China.
Micromachines (Basel). 2022 Dec 29;14(1):92. doi: 10.3390/mi14010092.
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.
在复杂表面上检测小缺陷极具挑战性，但对于确保工业领域的产品质量至关重要。然而，在现有方法的检测性能方面，在尺寸有限且特征表示极其微弱的小缺陷的定位和分割上仍存在巨大差距。为了解决上述问题，本文提出了一种用于在复杂表面上检测小缺陷的加权矩阵分解模型（WMD）。首先，基于缺陷图像中RGB通道的纹理特征构建加权矩阵，其目的是提高缺陷与背景之间的对比度。基于小缺陷的稀疏和低秩特性，然后将加权矩阵分别分解为对应于冗余背景和缺陷区域的低秩和稀疏矩阵。最后，使用自动阈值分割方法获得最优阈值，并在稀疏矩阵中准确分割出缺陷区域及其边缘。实验结果表明，所提出的模型在各种定量评估指标下均优于现有方法，具有广阔的工业应用前景。
