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

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.