Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns.

Abstract

Deep learning has shown potential in domains with large-scale annotated datasets. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images. In this work, we propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation. As a use case, we focus on wheat head segmentation. We synthesize a computationally annotated dataset-using a few annotated images, a short unannotated video clip of a wheat field, and several video clips with no wheat-to train a customized U-Net model. Considering the distribution shift between the synthesized and real images, we apply three domain adaptation steps to gradually bridge the domain gap. Only using two annotated images, we achieved a Dice score of 0.89 on the internal test set. When further evaluated on a diverse external dataset collected from 18 different domains across five countries, this model achieved a Dice score of 0.73. To expose the model to images from different growth stages and environmental conditions, we incorporated two annotated images from each of the 18 domains to further fine-tune the model. This increased the Dice score to 0.91. The result highlights the utility of the proposed approach in the absence of large-annotated datasets. Although our use case is wheat head segmentation, the proposed approach can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.

Plant phenotyping relevance

植物画像から穂を抽出するセマンティックセグメンテーション手法を開発し、複数データセットで性能検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's wheat head segmentation study provides two paper-specific public assets: the dataset (video frames, synthesized images, manual annotations) hosted on the authors' USask FTP server, and the authors' image synthesis pipeline code on GitHub. Both have explicit availability statements with public URLs.