Open AccessFeature fusion–based preprocessing for steel plate surface defect recognition
Author(s) -
Yong Tian,
Tian Zhang,
Qing Chao Zhang,
Yong Li,
Zhao Dong Wang
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020305
Subject(s) - sobel operator , artificial intelligence , prewitt operator , grayscale , pattern recognition (psychology) , computer science , binary image , feature (linguistics) , feature extraction , image gradient , image fusion , computer vision , channel (broadcasting) , preprocessor , image processing , edge detection , pixel , image (mathematics) , computer network , linguistics , philosophy
To address the problem of steel strip surface defect detection, a feature fusion-based preprocessing strategy is proposed based on machine vision technology. This strategy can increase the feature dimension of the image, highlight the pixel features of the image, and improve the recognition accuracy of the convolutional neural network. This method is based on commonly used image feature extraction operators (e.g., Sobel, Laplace, Prewitt, Robert, and local binary pattern) to process the defect image data, extract the edges and texture features of the defect image, and fuse the grayscale image processed by the feature operator with the original grayscale image by using three channels. To consider also computational efficiency and reduce the number of calculation parameters, the three channels are converted into a single channel according to a certain weight ratio. With this strategy, the steel plate surface defect database of NEU is processed, and fusion schemes with different operator combinations and different weight ratios for conversion to the single channel are explored. The test results show that, under the same network framework and with the same computational cost, the fusion scheme of Sobel:image:Laplace and the single-channel conversion weight ratio of 0.2:0.6:0.2 can improve the recognition rate of a previously unprocessed image by 3% and can achieve a final accuracy rate of 99.77%, thereby demonstrating the effectiveness of the proposed strategy.