According to the characteristics of glass defects, through analyzing the advantages and disadvantages of the traditional BP algorithm, an improved BP neural network recognition algorithm is applied to the glass defect classification and character recognition. Experimental results show that compared with traditional BP recognition algorithm, convergence speed of the algorithm is fast and the identification of false positives is low.
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