Semi-Supervised Metallographic Image Segmentation via Consistency Regularization and Contrastive Learning

Metallographic image segmentation is a core task towards the automation of metallographic analysis. Currently, the most advanced methods for this task generally employ supervised deep learning segmentation models that require a great number of pixel-level annotated images, while the annotation process is time-consuming and labor-intensive. In order to address this issue, a semi-supervised model called Con2Net is proposed in this work, which leverages unlabeled data to improve performance for metallographic image segmentation. The Con2Net model adopts a multi-decoder architecture, which enforces consistency constraint between each decoder's output and other decoders' soft pseudo labels produced by output sharpening. In addition, to mitigate the negative impact caused by sharpening on false predicted pixels, we adopt the sharpening operation only to accurately predicted pixels. For labeled pixels, comparing ground truth to filter out correctly predicted pixels is the simplest and most effective approach. For unlabeled pixels, a contrastive learning module is introduced, which encourages the model to have better intra-class compactness and inter-class dispersion in the feature space. Based on that, pseudo-labels for unlabeled pixels are obtained by calculating the maximum similarity of feature vectors, and then the accurately predicted unlabeled pixels could be filtered out. We conduct experiments on two public datasets for metallographic image segmentation, comparing the proposed Con2Net model with five state-of-the-art semi-supervised segmentation models on three semi-supervised data partition protocols. The results demonstrate that Con2Net not only outperforms the supervised baseline by a significant margin, but also achieves superior segmentation performance compared to other five semi-supervised models. Our source code is available at https://github.com/Siiimon2423/Con2Net.