A-Preprocessing and Labelling, B-Data augmentation, C- Feature extraction, D-Defects classification recognition
Fig. 4 Flow chart of surface defect identification and classification of Si3N4 ceramic bearing inner ring
⑴Preprocessing and labeling, the prerequisite for deep learning models to obtain better recognition and classification results is labeling the training set [20]. The contrast between the defective part and the background is enhanced by image preprocessing. LabelIMG is used to mark the surface defects, which are Pit, Crack, Wear and Snowflake. The XML file corresponding to the defect is generated. It is used to store the information of Si3N4 ceramic bearing roller inner ring surface defect, including the location, name, shape and boundary frame information of the defect. The boundary frame contains the pixel information of the surface defect.
⑵Data augmentation, large-scale dataset is beneficial to obtain better recognition and classification results. Online data augmentation expands the size of the dataset, improves the robustness of the target detection model, prevents the occurrence of overfitting.
⑶Feature extraction, the expanded data set is input from the input layer to the convolutional neural network and output from the output layer. And it is subsequently passed through CONV layer, pooling layer, CONV layer, pooling layer and fully connected layer. The images are transformed into two-dimensional matrices. Image features are preliminary extracted. Statistical features are extracted and image features are recorded according to regions.
⑷Defects classification recognition, smaller feature matrix is got. The features of each part are summarized. ReLU activation function is used to output feature values. The process of surface defect image recognition and classification has been completed.