mlsanityData to 7 Oct 2026

Phase-OTDR Event Detection Using Image-Based Data Transformation and Deep Learning

arXiv 2512.05830 ↗First posted 5 Dec 2025cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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: https://github.com/miralab-ai/Phase-OTDR-event-detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 83 references that are in the map. First posted on arXiv 5 Dec 2025.

Builds on

19 of 83 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  7. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  8. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  9. 2015An Introduction to Convolutional Neural Networkscited by 510
  10. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 396
  11. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  12. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
7 more
  1. 2023Synthetic ECG Signal Generation using Probabilistic Diffusion Modelscited by 22
  2. 2021ECG Heartbeat Classification Using Multimodal Fusioncited by 10
  3. 2015Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networkscited by 8
  4. 2021Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flowcited by 7
  5. 2022Remember to correct the bias when using deep learning for regression!cited by 4
  6. 2022An Intensity and Phase Stacked Analysis of Phase-OTDR System using Deep Transfer Learning and Recurrent Neural Networkscited by 1
  7. 2024Real-time Event Recognition of Long-distance Distributed Vibration Sensing with Knowledge Distillation and Hardware Accelerationcited by 1

Built on by

Not cited yet.