Multi-Scale and Multi-Match for Few-Shot Plant Disease Image Semantic Segmentation

Currently, deep convolutional neural networks have achieved great achievements in semantic segmentation tasks, but existing methods all require a large number of annotated images for training and do not have good scalability for new objects. Therefore, few-shot semantic segmentation methods that can identify new objects with only one or a few annotated images are gradually gaining attention. However, the current few-shot segmentation methods cannot segment plant diseases well. Based on this situation, a few-shot plant disease semantic segmentation model with multi-scale and multi-prototypes match (MPM) is proposed. This method generates multiple prototypes and multiple query feature maps, and then the relationships between prototypes and query feature maps are established. Specifically, the support feature and query feature are first extracted from the high-scale layers of the feature extraction network; subsequently, masked average pooling is used for the support feature to generate prototypes for a similarity match with the query feature. At the same time, we also fuse low-scale features and high-scale features to generate another support feature and query feature that mix detailed features, and then a new prototype is generated through masked average pooling to establish a relationship with the query feature of this scale. Subsequently, in order to solve the shortcoming of traditional cosine similarity and lack of spatial distance awareness, a CES (cosine euclidean similarity) module is designed to establish the relationship between prototypes and query feature maps. To verify the superiority of our method, experiments are conducted on our constructed PDID-5(i) dataset, and the mIoU is 40.5%, which is 1.7% higher than that of the original network.