Open AccessFabric defect detection method based on Cascade Deep Support Vector Data Description
Author(s) -
Xupeng Wang,
Yueyang Li,
Huiwu Luo
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012079
Subject(s) - anomaly detection , support vector machine , artificial intelligence , cascade , computer science , pattern recognition (psychology) , outlier , image (mathematics) , engineering , chemical engineering
The Fabric defect detection method based on Cascade Deep Support Vector Data Description (SVDD) is proposed in this paper. The method describes the data by Deep SVDD to realize the correct evaluation between the normal fabric images and the images with defects in the high dimensional space. Combining with the cascading method, the proposed algorithm can be used to detect minor defects more accurately and quickly. We take the anomaly score as the evaluation result of the testing image, then the Median Absolute Deviation (MAD) outlier detection method is applied to get the final defect detection results. This newly developed method can be trained with only a small amount of defect-free samples, which greatly reduces manual intervention. A variety types of defects in the general fabric images can be detected efficiently. The experimental results demonstrate that the proposed model has good global performance and high recall rate.