Knowledge Transfer Based Spatial Embedding Network for Plant Leaf Instance Segmentation

Abstract

This paper proposes a method to segment plant leaves using knowledge distillation. Unlike the existing knowledge distillation method aimed at lightening the model, we use knowledge distillation to achieve good performance even with a small amount of dataset. Plants have many leaves, and each leaf is very small. Therefore, the leaf instance segmentation is performed based on spatial embedding. The teacher network is trained with a large dataset and then distills its segmentation knowledge into the student network. Two types of knowledge are distilled from the teacher network: attention distillation and region affinity distillation. The results of the experiment demonstrate that better instance segmentation can be achieved when knowledge distillation is used.