A Fuzzy Support Vector Machine-Enhanced Convolutional Neural Network for Recognition of Glass Defects

The existence of glass defects seriously affects the quality of glass products. Traditional glass defect recognition methods based on convolutional neural networks (CNNs) suffer from a long training time and low recognition accuracy. By introducing convolutional auto-encoder (CAE) into a CNN, we propose an auto-encoding convolutional neural network, and pre-train the convolution kernel by CAE, thereby reducing the training time caused by randomly initializing the convolution kernel. At the same time, in order to cope with the over-fitting problem caused by a small sample of glass defect dataset, we employ a fuzzy support vector machine (FSVM) instead of the Softmax classifier to classify glass defects. Furthermore, we propose a multi-channel auto-encoding convolutional neural network model to deal with misidentification of inclusions and tumor type defects due to small differences in feature space. The model takes a defect image and its enhanced image as input, and averages the output of each channel as the final output, thus achieving accurate recognition of glass defects. Experimental results show that the glass defect recognition method based on auto-encoding convolutional neural network can greatly reduce the training time while maintaining the same classification accuracy rate. The glass defect recognition method based on multi-channel auto-encoding convolutional neural network achieves a recognition rate of 95% for both inclusion and tumor type defects. As a result, and the overall recognition rate is increased from 92.6% using a single channel to 97%.