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<p>Distributed Acoustic Sensing (DAS) has emerged as a promising tool for real-time traffic monitoring in densely populated areas. In this paper, we present a new approach that integrates DAS data with co-located, calibrated video recordings. We use YOLO-derived vehicle location and classification from video inputs as labeled data to train a detection and classification neural network that uses DAS data only. The model is applied in areas with and without video coverage. It achieves about <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_14928_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">\(92\%\)</EquationSource> </InlineEquation> success in detection and classification, and about <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_14928_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="35" /> </InlineMediaObject> <EquationSource Format="TEX">\(3.6\%\)</EquationSource> </InlineEquation> false alarm rate when compared to YOLO outputs. We illustrate the model’s application in monitoring a week of traffic, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors and cameras, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our datasets.</p>

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A fiber-optic traffic monitoring network trained with video inputs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月7日

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  • Khen Cohen,
  • Liav Hen,
  • Ariel Lellouch

摘要

Distributed Acoustic Sensing (DAS) has emerged as a promising tool for real-time traffic monitoring in densely populated areas. In this paper, we present a new approach that integrates DAS data with co-located, calibrated video recordings. We use YOLO-derived vehicle location and classification from video inputs as labeled data to train a detection and classification neural network that uses DAS data only. The model is applied in areas with and without video coverage. It achieves about \(92\%\) success in detection and classification, and about \(3.6\%\) false alarm rate when compared to YOLO outputs. We illustrate the model’s application in monitoring a week of traffic, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors and cameras, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our datasets.