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Conjugate gradient neural network‐based online recognition of glass defects

Online recognition of glass defects plays an important role in improving the quality of glass products. This paper presents a conjugate gradient neural network‐based method, which combines phase map and the defective image for recognition of glass defects. The boundary coordinates of the connected defect region are calculated and used to extract the defect region in the defective image correspondingly. The piecewise linear gray‐level transformation is designed to reduce the noise and to enhance the signal‐to‐noise ratio of the defective image. The second iteration segmentation based on gray range is applied to calculate the low and high thresholds, and the ternary‐valued defective image is acquired. The seven features calculated by Hu invariant moment and four features extracted from the ternary‐valued defective image are used as inputs of the conjugate gradient neural network to recognize the defect type. Experimental results show that the accuracy of the recognition reaches up to 93%. Copyright © 2016 John Wiley & Sons, Ltd.