Classification algorithm of positive and sub-group tobacco leaves in natural state based on lightweight SE-PPM.

[Objective] A lightweight SE-PPM algorithm (SAPMDSNet) was proposed to solve the problems of slow speed, misclassification and difficulty in feature extraction in traditional positive and subgroup classification of natural state tobacco leaves. [Method] Based on the lightweight ShuffleNetV2 net-work, the training speed of the network model was accelerated by reducing the network convolution depth and evolving the activation function. Then, the channel attention SE module was introduced to enhance the haracteristic differences between channels, improve the representation ability of the network model and a-void the group misclassification caused by the regionalization. Finally, the pyramid pooling module PPM was embedded to integrate the exposure characters and global information by aggregating context information, and the in-house tobacco leaf data set was used and compared with other models. [Result] The SAPMDSNet network model achieved relatively high classification results with accuracy of 91.09% and FLOPs of 151.70 M. Compared with the original ShuffleNetV2 model and the lightweight GhostNet model, the FLOPs were slightly increased by 2.65% and 2.84%, and the accuracy was increased by 2.72% and 21.13 %, respectively. MobileNetV2, DenseNet and SqueezeNet achieved recognition accuracies of 87.02%, 89.53% and 87.60%, which were close to the proposed model, but their FLOPs were significantly higher. [Conclusion] The constructed SAPMDSNet network model improved the classification accuracy of positive and sub-group tobacco leaves in natural state with better overall performance, which provides a new method for primary screening of tobacco leaves. [ABSTRACT FROM AUTHOR]