Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning
Muhammet Cagri Yeke, Samil Sirin, Kivilcim Yuksel, Abdurrahman Gumus

TL;DR
This paper introduces a novel image-based deep learning approach for classifying optical fiber events using Phase-OTDR data, achieving high accuracy and demonstrating the effectiveness of transforming 1D signals into grayscale images for improved analysis.
Contribution
It presents a new method of converting Phase-OTDR 1D data into multi-channel images for enhanced event classification with transfer learning models.
Findings
Achieved over 98% classification accuracy with EfficientNetB0 and DenseNet121.
Validated models with 5-fold cross-validation showing high reliability.
Provided publicly available dataset and code for further research.
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…
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Taxonomy
TopicsAdvanced Fiber Optic Sensors · Optical Network Technologies · Advanced Fiber Laser Technologies
