关键词: 油茶果, 深度学习, 分选, SE-ResNet18模型, 注意力机制

Abstract: When the shell is mixed with the tea seed, the traditional mechanical separation will still be doped with the shell, and the cleaning rate needs to be improved. In this paper, by comparing different layers of ResNet, it is found that in the current shell seed experiment samples, ResNet18 has the lowest average training time of each iteration compared with other models, and the highest average accuracy of verification set, which is more superior to other CNN classification models. In order to further improve the sorting efficiency, the attention mechanism was introduced into the ResNet18 network and the results showed that the average time per iteration in the training process of SE-ResNet18 model decreased from 1.31 s to 1.13 s by shortening 0.18 s, and the average accuracy of the validation set was 98.88% by improving 1.4 percentage points, compared with the model before improvement, and the overall accuracy of the test set was 98.43%, which was 13 percentage points higher than that of the original model, indicating that the use of ResNet18 model combined with the attention mechanism was feasible for the sorting of oil tea fruit shells and tea seeds, providing a new theoretical basis and thinking direction for the sorting method of camellia fruit shells and seeds.

Key words: camellia fruit, deep learning, sorting, SE-ResNet18 model, attention mechanism