| Title |
ssembly Defect Classification of SMD Components by Cascade Convolutional Neural Network |
| Authors |
류종현(Jong-Hyun Ryu) ; 김영규(Young-Gyu Kim) ; 박태형(Tae-Hyoung Park) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.10.1236 |
| Keywords |
Surface mount device; Deep learning; Defect classification |
| Abstract |
In this paper; we propose the classification method of assembly defects in the surface mount technology process. We used a cascade convolution neural network which two convolutional neural networks were merged into one network; for assembly defect classification. The first network classifies whether the surface mount device (SMD) is defect or not. The second network classify the result of the first network more detail. We classified the SMD defects as six types using a cascade convolution neural network. Experiment result shows that the proposed method can optimize memory usage and improve classification accuracy compared to previous methods. |