JCS: An Explainable Surface Defects Detection Method for Steel Sheet by Joint Classification and Segmentation
| Autor: | Ketao Cui, Zhiqiang Hao, Huaiguang Liu, Shiyang Zhou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Surface (mathematics)
class-specific and shared dictionary learning Similarity (geometry) General Computer Science Texture (cosmology) Computer science business.industry surface defects of steel sheet General Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image segmentation Joint classification and segmentation for image Matrix decomposition Image (mathematics) TK1-9971 Discriminative model Computer Science::Computer Vision and Pattern Recognition General Materials Science Segmentation Artificial intelligence Electrical engineering. Electronics. Nuclear engineering Electrical and Electronic Engineering business double low-rank matrix decomposition |
| Zdroj: | IEEE Access, Vol 9, Pp 140116-140135 (2021) |
| ISSN: | 2169-3536 |
| Popis: | For surface defect images that captured from a practical steel production line, different shape, size, location and texture of defect object may cause inter-class similarity and intra-class difference of defect images. Despite attractive results have been achieved in some surface methods for defect classification and segmentation, it is still far from meeting the needs of real-world applications due to lack of adaptiveness of these methods. Considering the surface defect image can be decomposed into defect foreground image and defect-free background image, the paper develops a novel joint classification and segmentation (JCS) approach to perform surface defects detection for steel sheet. It comprises of the classification method based on a class-specific and shared discriminative dictionary learning (CASDDL) and the segmentation method based on a double low-rank based matrix decomposition (DLMD), respectively. For the proposed CASDDL method, we learn a shared sub-dictionary as well as several class-specific sub-dictionaries to explicitly capture common information shared by all classes and class-specific information belonging to corresponding class. We adopt a mutual incoherence constrain for each sub-dictionary, a Fisher-like discriminative criterion and low-rank constrain on coding vector to improve the discriminative ability of learned dictionary. For the proposed DLMD method, we formulate the segmentation task as a double low-rank based matrix factorization problem, and the Laplacian and sparse regularization terms are introduced into the matrix decomposition framework. Experimental results demonstrate that our proposed JCS method achieve a comparable or better performance than the state-of- the-art methods in classifying and segmenting surface defects of steel sheet. |
| Databáze: | OpenAIRE |
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