GrotUNet: a novel leaf segmentation method.

In the field of biology, the current leaf segmentation method still has problems such as missed inspections and duplication in the number of large, dense, mutual obstruction and vague division tasks. The reason for the above is that image semantic extraction is not satisfactory and semantic parsing is still insufficient. To address the above problems, this paper proposes GrotUNet, a novel leaf segmentation method that can be trained end-to-end. The algorithm is reconstructed in three aspects: semantic feature coding, hopping connectivity, and multiscale upsampling fusion. The semantic coding structure consists of GRblock, WGRblock, and OTblock modules. The former two make full use of the design ideas of GoogLeNet parallel branching and Resnet residual connectivity, while the latter further mines the fine-grained semantic information distributed in the feature space on the feature map after extraction by the WGRblock module to make the feature expression richer. Unlike UNet++ dense connectivity, jump connection reconstruction only uses