Glass surface defect detection method based on multiscale convolution neural network
Convolutional neural network is widely used in image processing. In order to effectively inspect glass surface defects in production activities, the principle of machine learning based on convolutional neural network was analyzed. An image recognition model based on Multiscale Convolution Neural Network (MCNN) was proposed. Then, the application of MCNN model in the identification of glass surface defects was studied, and comparison experiments were carried out by using different algorithms and classifiers. Furthermore, confusion matrix and F1 values to evaluate learner performance were used to evaluate the performance of learner. Experiment results showed that the designed MCNN was more accurate than the traditional Convolutional Neural Networks (CNN) recognition method, especially in the recognition accuracy of scratch defects and impurity defect images, F1 values were increased by more than 5.0%. Obviously, by comparing with the traditional CNN, MCNN is superior in the overall recognition accuracy of glass defect detection.