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 ima

SUBMITTER: Najafian K 

PROVIDER: S-EPMC10013790 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature