Fig. 1. Structure of the B-CNN

Fig. 2. Process of the gradient calculation

Fig. 3. Basic structure of the SENet

Fig. 4. Flow chart of the SENet

Fig. 5. Improved network structure

Fig. 6. Structure of the residual unit

Fig. 7. Images of press-plate in different states. (a) guan; (b) kai; (c) NS1; (d) NS2

Fig. 8. Flow chart of network training

Fig. 9. Confusion matrix of different methods. (a) Our method; (b) B-CNN

Fig. 10. Grad-CAM diagrams with different opening and closing angles of the press-plate

Fig. 11. Recognition results of different methods. (a) NS1; (b) NS2; (c) guan; (d) kai

Fig. 12. Accuracies of different methods

Fig. 13. Loss rates of different methods

Fig. 14. Accuracies of different methods in the test set