# Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
## 4 Experiments and Discussions
### Discussion  
Based on the precision comparison of various algorithms in Section 4.2.3, the two deep learning-based algorithms (the ST-SDM and the CNN-SDM) exhibit higher AVMEs but significantly lower RMSEs compared to traditional algorithms. This indicates that deep learning algorithms possess superior stability over traditional methods. Future work will focus on designing more accurate deep learning algorithms to address the sub-pixel displacement measurement problem more effectively. Additionally, the ST-SDM demonstrates higher computational accuracy than the CNN-SDM. The primary reason is that the CNN-SDM employs the traditional Convolutional Neural Network (CNN) approach, whereas our ST-SDM utilizes the Swin Transformer. The Swin Transformer, with its unique hierarchical architecture and shifted window mechanism, is more efficient in capturing local features and global context information when handling image classification tasks, thereby achieving higher performance compared to traditional CNN methods. Figure 12: The AVME and relative error of two models each with a 0.1 mm shift. According to the comparison experiments in Section 4.2.4, the ST-SDM has higher computational efficiency than the CNN-SDM. This is made available thanks to the fact that we employ a Swin Transformer structure in each level of classification, which we briefly describe in Section 2.2. The comparison of a Swin Transformer structure with a ViT and a CNN in terms of computational speed and computational storage requirements has been discussed in detail in the literature [36; 37]. The square dataset expansion method provides a universal and effective solution for expanding speckle pattern training datasets, especially when the number of training samples is extremely limited. This method can significantly enhance the scale and diversity of the dataset, thereby creating more favorable conditions for model training. However, this augmentation approach may also have certain limitations. Specifically, the network model trained using this method may lack sufficient generalization ability. Although the model's performance on the training dataset has been verified to be satisfactory through previous experiments, its performance on entirely new, unseen data may not be guaranteed, which could affect the practical application value of the model. Although the ST-SDM algorithm proposed in this paper provides a novel solution for the sub-pixel displacement measurement problem, the current experimental research is not yet comprehensive. For instance, the algorithm's adaptability under varying lighting conditions and other constraints, as well as its real-time performance, have not been fully evaluated. Future research could integrate adaptive illumination normalization (e.g., Retinex-based preprocessing or deep learning-driven intensity correction) and hybrid architectures that combine lightweight neural networks with physics-based speckle correlation models to balance precision and efficiency. Strategies such as hardware acceleration (FPGA/GPU deployment) and domain-generalizable training with synthetic speckle data augmented with realistic noise and lighting perturbations could enhance robustness. In addition, while vision transformers (ViTs) have demonstrated remarkable success in diverse computer vision tasks (e.g., classification, detection, and segmentation), their potential to enhance digital speckle correlation methods (DSCMs) remains unexplored--a gap offering significant opportunities to improve deformation analysis in complex environments.