Investigation on Fiber Torsion Sensing Mechanism Based on Multimode Fiber Speckle and Deep Learning

This study proposes a high-precision fiber speckle-based torsion sensor utilizing a multimode fiber (MMF) structure and the ResNet34-SA-MB (ResNet34 with Self-Attention and Multi-Branch) model. The sensor leverages deep learning techniques to establish a mapping between fiber speckle patterns and torsion angles while integrating residual networks, self-attention mechanisms, and multi-branch structures to enhance feature extraction and prediction accuracy. Experimental results demonstrate that the proposed sensor achieves 100% accuracy within ± 1° and ± 0.5° error ranges in the known-angle test set (test set I). In the unknown-angle test set (test set II), it attains an accuracy of 89.67% within a ± 1° error range, exhibiting strong generalization capability. Compared to conventional fiber torsion sensing schemes, this approach offers advantages such as structural simplicity, low cost, and a wide measurement range. It is well-suited for structural health monitoring, robotic arm control, and other engineering applications, providing a novel solution for high-precision and cost-effective torsion measurements.