Steel Surface Defect Classification Based on Discriminant Manifold Regularized Local Descriptor

• Main Authors: Jiuliang Zhao, Yishu Peng, Yunhui Yan
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Steel surface defect classification; local descriptor; discriminant manifold learning; manifold metric
• Online Access: https://ieeexplore.ieee.org/document/8540001/

Steel surface demonstrates various sorts of defects due to the production technique and environment. The appearance of defect is in much more random pattern than that of the normal texture image. Therefore, it is challenging to capture the discriminant information to categorize the defects. The defect image is out of image registration in grayscale, and thus, the local descriptor is inclined to be utilized for feature extraction. In the previous works, involving a local descriptor for categorizing the defect images, the thresholding operator participates in the hand-crafted feature extraction, such as local binary patterns and histogram of oriented gradient, leading to sub-optimal features. By introducing the learning mechanism into the construction of local descriptor, a novel algorithm named discriminant manifold regularized local descriptor (DMRLD) is proposed to conduct the defect classification task in this paper. First, the DMRLD computes the dense pixel difference vector (DPDV) to draw the local information of defect images. Then, the manifold of these DPDVs can be constructed by searching for a number of linear models to represent the feature. In order to enhance the discriminant ability of the feature, a projection on the manifold is learned for achieving a low-dimensional subspace. Finally, the manifold distance defined in the subspace can accomplish the matching task to get the category of the defect image. The proposed algorithm is first applied on the Kylberg texture dataset to evaluate the texture feature extraction performance, and then the experiments on the real steel surface defect dataset are conducted to illustrate the effectiveness of DMRLD compared with other local descriptors.