# LeafMask: Towards Greater Accuracy on Leaf Segmentation

arXiv: https://arxiv.org/abs/2108.03568 · HTML: https://mlsanity.com/paper/2108.03568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [CenterMask : Real-Time Anchor-Free Instance Segmentation](https://mlsanity.com/paper/1911.06667/) (2019)
- [BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation](https://mlsanity.com/paper/2001.00309/) (2020)
- [Semantic Instance Segmentation with a Discriminative Loss Function](https://mlsanity.com/paper/1708.02551/) (2017)
- [Efficient piecewise training of deep structured models for semantic segmentation](https://mlsanity.com/paper/1504.01013/) (2015)
- [Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade](https://mlsanity.com/paper/1704.01344/) (2017)
- [Recurrent Instance Segmentation](https://mlsanity.com/paper/1511.08250/) (2015)
- [Deep Leaf Segmentation Using Synthetic Data](https://mlsanity.com/paper/1807.10931/) (2018)
- [Recurrent Neural Networks for Semantic Instance Segmentation](https://mlsanity.com/paper/1712.00617/) (2017)
- [Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants](https://mlsanity.com/paper/1903.08583/) (2019)

## Built on by (most cited first, 1 of 1)

- [Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf Segmentation](https://mlsanity.com/paper/2305.09418/) (2023)

## Abstract (arXiv)

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.