期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 1, 页码 141-150出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2917522
关键词
Feature extraction; Inspection; Sparse matrices; Surface texture; Image segmentation; Task analysis; Visualization; Entity sparsity; feature extraction; intrinsic prior; surface defect detection
类别
资金
- Fundamental Research Funds for the Central Universities [2018YJS042, 2016JBZ006]
- Open Fund of Beijing Intelligent Logistics System Collaborative Innovation Center [2018KF-07]
- Shanghai Research Program [17511102900]
Computer vision based methods have been widely used in surface defect inspection. However, most of these approaches are task specific, and it is hard to transfer them to similar detection scenarios. This paper proposes an entity sparsity pursuit (ESP) method to identify surface defects. Based on the observation that surface image textures usually form a low-rank structure and the structure can be violated by the presence of rare defects, we formulate the detection task as a low-rank and ESP problem. To alleviate the feature shortage issue existed in industrial gray-scale images, we customize a kind of intuitive features for surface defect inspection. Different from previous work utilizing complicated regularization terms, we resort to mine intrinsic priors of defect images, which can be neatly incorporated into the designed architecture. The proposed model is compact and able to detect surface defects in an unsupervised manner. To fully evaluate the presented method, we conduct a series of experiments using three real-world and one synthetic defect datasets. Experimental results demonstrate that ESP outperforms state-of-the-art methods.
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