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DEVELOPMENT OF A CONVOLUTIONAL LAYER OF A DEEP NEURAL NETWORK FOR DETECTING DEFECTS IN ROLLED METAL

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A convolutional layer of a deep neural network, designed to determine defects in rolled metal products, is considered. To determine the defect, it is proposed to use algorithms for the segmentation of flaw detection images and several types of filtering within the convolutional layer. Filtering is based on the use of combined convolution algorithms with different initial masks. To minimize the error at the output of the convolutional layer, the GELU activation function is used. Results of the experiments are presented.

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