针对基于端面成像的偏振轴检测方法光照鲁棒性不强、无法同时检测多根保偏光纤偏振轴的问题, 提出一种基于Faster RCNN的检测方法。训练Faster RCNN神经网络模型, 通过参数调优增强了应力区识别的鲁棒性, 采用基于Zernike矩算法对应力区边缘点进行亚像素级定位以提高测量精度, 分析了测量精度与误差的关系。仿真与实验表明, 该方法的测量精度达到±0.1°, 在增强光照鲁棒性的同时实现了对多根保偏光纤偏振轴的检测。

Aiming at the problem that the polarization axis detection method based on end surface is not robust to light and can not detect polarization axes of several polarization maintaining fibers at the same time, a detection method based on Faster RCNN was proposed. Firstly, the Faster RCNN neural network model was trained and the robustness of stress rods recognition was enhanced by parameter tuning. Then, the edge points of stress rods were detected by the subpixel edge detector based on Zernike moment to improve the measurement accuracy. Lastly, the relationship between measurement accuracy and error is analyzed. Experimental results show that the measurement accuracy of the proposed method is less than ±0.1°, the robustness of light is enhanced and the detection of polarization axes of multiple polarization maintaining fibers is realized at the same time.