Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance

TL;DR

This paper introduces a semi-supervised segmentation framework for concrete aggregate images that combines consensus regularisation with prior knowledge constraints, improving performance over traditional methods.

Contribution

The paper proposes a novel semi-supervised segmentation method with a lightweight architecture and additional prior-based losses to address imbalanced label distributions.

Findings

Outperforms purely supervised segmentation methods.

Enhances segmentation accuracy using prior knowledge constraints.

Effective on concrete aggregate dataset.

Abstract

In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to significantly improve the performance of purely supervised segmentation learning. However, the consensus principle behind consistency training has at least one drawback, which we identify in this paper: imbalanced label distributions within the data. To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge. Specifically, we propose a light-weight architecture consisting of a shared encoder and a main decoder, which is trained in a supervised manner. An auxiliary decoder is added as additional branch in order to make use of unlabelled data based on consensus training, and we add additional constraints derived from prior information on the class distribution and on auto-encoder regularisation. Experiments performed on our "concrete aggregate dataset" presented in this paper demonstrate the effectiveness of the proposed approach, outperforming the segmentation results achieved by purely supervised segmentation and standard consistency training.