A lightweight convolutional neural network for recognition and classification for Si3N4 chip substrate surface defects

Abstract

For the low accuracy and poor efficiency for surface defects detection, identifying and locating defects becomes a key process. However, traditional inspection methods cannot solve these problems. A lightweight improved YOLOX network with attention mechanism (GhostC-YOLOX) for Si3N4 ceramic chip substrate surface defect recognition and classification is proposed. For liner regression, detection and classification tasks, SimOTA label assignment strategy is combined with Moscia and MixUp data augmentation. Dynamic allocation of positive samples is achieved and the dataset is expanded. To the training process, the loss function is optimized. L2 regularization term is introduced to improve the generalization ability. Aiming to generate more feature maps with fewer parameters, the GhostNet structure is embed into the Neck of YOLOX, which reduces the computational cost while maintaining performance. To improve the defect detection accuracy, the lightweight Attention mechanism CBAM(Convolutional Block Attention Module) is integrated into the Backbone tail of YOLOX. The attention is allocated to the two dimensions simultaneously. The detection accuracy of Si3N4 ceramic chip substrate is 96.47%. Compared with YOLOX and Faster RCNN models, the detection accuracy is improved by 2.648% and 18.08% respectively. Moreover, the parameter size of the method is only 5.034 MB. Its detection performance is obviously ahead of other detection methods.