Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks

TL;DR

This paper introduces a self-supervised hybrid deep learning network for fiber signal denoising and vehicle detection in optical fiber-based sensing systems, improving performance without requiring labeled data.

Contribution

The paper proposes a novel self-supervised hybrid deep learning network combining autoencoder and LSTM for fiber signal denoising and feature extraction in ITS applications.

Findings

Outperforms spatial-domain DAE in denoising performance

Effective vehicle detection and tracking from fiber signals

Validated on real highway tunnel dataset

Abstract

With the applicability of optical fiber-based distributed acoustic sensing (DAS) systems, effective signal processing and analysis approaches are needed to promote its popularization in the field of intelligent transportation systems (ITS). This paper presents a signal denoising algorithm using a hybrid deep-learning network (HDLNet). Without annotated data and time-consuming labeling, this self-supervised network runs in parallel, combining an autoencoder for denoising (DAE) and a long short-term memory (LSTM) for sequential processing. Additionally, a line-by-line matching algorithm for vehicle detection and tracking is introduced, thus realizing the complete processing of fiber signal denoising and feature extraction. Experiments were carried out on a self-established real highway tunnel dataset, showing that our proposed hybrid network yields more satisfactory denoising performance than Spatial-domain DAE.