Application of semi-supervised Mean Teacher to rock image segmentation - Jiashan Li; Yuxue Wang (Image Analysis and Stereology)
Accurate segmentation of rock images is crucial for studying the internal structure and properties of rocks. To address the issue of requiring a large number of labeled images for model training in traditional image segmentation methods, this paper proposes an improved semi-supervised Mean Teacher algorithm based on ResNet34-UNet. This method achieves relatively accurate rock image segmentation using only a small amount of labeled data. Initially, we use ResNet34-UNet as the base model to create Student Model and Teacher Model with identical structures. Then, we introduce self-attention mechanism into the semi-supervised Mean Teacher algorithm to further enhance its performance in rock image segmentation. Finally, by comparing the performance of supervised and semi-supervised Mean Teacher algorithms on image segmentation tasks, we validate the effectiveness of semi-supervised learning in rock image segmentation.