关键词: 古筝面板, 泡桐导管图像识别, 残差连接, 深度可分离卷积

Abstract:

The rise of deep learning and transfer learning provides a new direction for tree species identification. However, there are still challenges in wood identification among different qualities within the same tree species. In order to improve the quality grading status of zither panel, a deep residual network model is designed. Firstly, the data set is divided and the training samples are expanded by data enhancement technology. Then, the pre-trained model on ImageNet is transferred to this problem. In order to extract the plate image features efficiently, a new deep feature extraction part is added after the pre-training model, which combines residual connection and depth separable convolution. It can not only enhance feature reuse and alleviate gradient disappearance, but also help to extract the deep features of the image. Finally, in order to improve the robustness of the model in the training process, LeakyReLU function is used instead of ReLU function to avoid neuron death. Compared with other mainstream methods, the model can save the time of zither quality classification and improve the recognition accuracy.

Key words: zither panel, paulownia catheter image recognition, residual connection, depth separable convolution