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Feature fusion–based preprocessing for steel plate surface defect recognition

State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China

Special Issues: Learning based Computational Bioinformatics for Healthcare Big Data

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.
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Keywords convolutional neural network; image fusion; feature extraction; steel plate surface defect; image recognition

Citation: Yong Tian, Tian Zhang, Qingchao Zhang, Yong Li, Zhaodong Wang. Feature fusion–based preprocessing for steel plate surface defect recognition. Mathematical Biosciences and Engineering, 2020, 17(5): 5672-5685. doi: 10.3934/mbe.2020305

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