A Lightweight Semantic Segmentation Model of Wucai Seedlings Based on Attention Mechanism

Accurate wucai seedling segmentation is of great significance for growth detection, seedling location, and phenotype detection. To segment wucai seedlings accurately in a natural environment, this paper presents a lightweight segmentation model of wucai seedlings, where U-Net is used as the backbone network. Specifically, to improve the feature extraction ability of the model for wucai seedlings of different sizes, a multi-branch convolution block based on inception structure is proposed and used to design the encoder. In addition, the expectation “maximizationexpectation” maximization attention module is added to enhance the attention of the model to the segmentation object. In addition, because of the problem that a large number of parameters easily increase the difficulty of network training and computational cost, the depth-wise separable convolution is applied to replace the original convolution in the decoding stage to lighten the model. The experimental results show that the precision, recall, MIOU, and F1-score of the proposed model on the self-built wucai seedling dataset are 0.992, 0.973, 0.961, and 0.982, respectively, and the average recognition time of single frame image is 0.0066 s. Compared with several state-of-the-art models, the proposed model achieves better segmentation performance and has the characteristics of smaller-parameter scale and higher real-time performance. Therefore, the proposed model can achieve good segmentation effect for wucai seedlings in natural environment, which can provide important basis for target spraying, growth recognition, and other applications.