Can I read LeafMask: Towards Greater Accuracy on Leaf Segmentation on EtoBox?

LeafMask: Towards Greater Accuracy on Leaf Segmentation by Guo, Ruohao; Qu, Liao; Niu, Dantong; Li, Zhenbo; Yue, Jun is a scholarly article available to read on EtoBox.

What is LeafMask: Towards Greater Accuracy on Leaf Segmentation about?

Leaf segmentation is the most direct and effective way for high-throughput plant phenotype data analysis and quantitative researches of complex traits. Currently, the primary goal of plant phenotyping is to raise the accuracy of the autonomous phenotypic measurement. In this work, we present the LeafMask neural network, a new end-to-end model to delineate each leaf region and count the number of leaves, with two main components: 1) the mask assembly module merging position-sensitive bases of each predicted box after non-maximum suppression (NMS) and corresponding coefficients to generate original masks; 2) the mask refining module elaborating leaf boundaries from the mask assembly module by the point selection strategy and predictor. In addition, we also design a novel and flexible multi-scale attention module for the dual attention-guided mask (DAG-Mask) branch to effectively enhance information expression and produce more accurate bases. Our main contribution is to generate the final improved masks by combining the mask assembly module with the mask refining module under the anchor-free instance segmentation paradigm. We validate our LeafMask through extensive experiments on Leaf

Author

Guo, Ruohao; Qu, Liao; Niu, Dantong; Li, Zhenbo; Yue, Jun

Published

2021

Language

EN