Abstract
This study focuses on event detection in optical fibers, specifically classifying six events using the Phase-OTDR system. A novel approach is introduced to enhance Phase-OTDR data analysis by transforming 1D data into grayscale images through techniques such as Gramian Angular Difference Field, Gramian Angular Summation Field, and Recurrence Plot. These grayscale images are combined into a multi-channel RGB representation, enabling more robust and adaptable analysis using transfer learning models. The proposed methodology achieves high classification accuracies of 98.84% and 98.24% with the EfficientNetB0 and DenseNet121 models, respectively. A 5-fold cross-validation process confirms the reliability of these models, with test accuracy rates of 99.07% and 98.68%. Using a publicly available Phase-OTDR dataset, the study demonstrates an efficient approach to understanding optical fiber events while reducing dataset size and improving analysis efficiency. The results highlight the transformative potential of image-based analysis in interpreting complex fiber optic sensing data, offering significant advancements in the accuracy and reliability of fiber optic monitoring systems. The codes and the corresponding image-based dataset are made publicly available on GitHub to support further research: this https URL.
Abstract (translated)
这项研究专注于基于光纤的事件检测，特别使用相位-OTDR（Phase-Optical Time Domain Reflectometer）系统对六种不同的事件进行分类。提出了一种新颖的方法来增强相位-OTDR数据的分析能力，通过运用Gramian角度差异场、Gramian角度和场以及递归图等技术将1D数据转换为灰度图像。这些灰度图像被组合成多通道RGB表示形式，从而能够利用迁移学习模型进行更强大且适应性更强的分析。 所提出的这种方法在使用EfficientNetB0和DenseNet121两种模型时分别实现了高达98.84%和98.24%的高分类准确率。通过五倍交叉验证过程确认了这些模型的可靠性，测试准确性分别为99.07%和98.68%。 利用一个公开可用的相位-OTDR数据集，该研究展示了理解光纤事件的有效方法，并且在减少数据量的同时提高了分析效率。研究结果突显了基于图像分析技术在解释复杂光纤传感数据方面的变革潜力，为光纤监测系统的准确性和可靠性提供了显著的进步。 为了支持进一步的研究，在GitHub上公开了代码和相应的基于图像的数据集：[此链接](this https URL)。
URL
https://arxiv.org/abs/2512.05830
