Abstract
Aiming at the problems of low classification accuracy and overfitting caused by the limited number of particleboard image samples, a Capsule Network algorithm based on the improved CBAM (Convolutional Block Attention Module) attention model is proposed. The improved algorithm utilizes the GELU equation to improve the CBAM attention model and incorporates it into the convolutional layer of the Capsule Network. In this way, the improved algorithm optimizes the feature maps of surface defects and, meanwhile, improves the training efficiency and stability of the model. The improved algorithm alleviates the overfitting problem by adding a dropout layer, which makes the model more suitable for small sample classification. The effectiveness of the method proposed in this paper is verified by classification experiments on the dataset of particleboard surface defect images.
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