LeafMask: Towards Greater Accuracy on Leaf Segmentation

TL;DR

LeafMask is a novel neural network designed for precise leaf segmentation, combining mask assembly and refinement modules with a multi-scale attention mechanism, achieving state-of-the-art accuracy in plant phenotyping tasks.

Contribution

The paper introduces LeafMask, an end-to-end, anchor-free segmentation model with a new mask assembly and refinement approach, plus a multi-scale attention module for improved leaf boundary detection.

Findings

Achieved 90.09% BestDice score on LSC dataset.

Outperformed existing state-of-the-art leaf segmentation methods.

Validated effectiveness of multi-scale attention in leaf boundary delineation.

Abstract

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 Segmentation Challenge (LSC) dataset. Our proposed model achieves the 90.09% BestDice score outperforming other state-of-the-art approaches.