用摄像头标注的车辆信息训练光纤声感模型，实现高精度交通监测。
Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs
- 用摄像头提供的车辆位置分类数据标注DAS信号，训练纯声学检测网络。
- 检测与分类准确率超94%，误报率仅1.2%。
- 兼顾隐私保护、低成本与可扩展性，适合智慧城市建设。
分布式声学传感（DAS）已成为密集城区实时交通监控的有力工具。本文提出一种新方法，将DAS数据与同位置视觉信息融合。利用摄像头输入生成的YOLO车辆定位与分类结果作为标签，训练仅使用DAS数据的检测与分类神经网络。模型在检测与分类任务上表现超过94%，误报率约为1.2%。通过一周的交通监测应用，获得统计规律，为未来智慧城市发展提供参考。该方法展示了光纤传感器与视觉信息结合的潜力，强调实用性、可扩展性、隐私保护及低基础设施成本。为推动后续研究，我们公开了数据集。
原文摘要 · Abstract (English)
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 novel concept that integrates DAS data with co-located visual information. We use YOLO-derived vehicle location and classification from camera inputs as labeled data to train a detection and classification neural network utilizing DAS data only. Our model achieves a performance exceeding 94% for detection and classification, and about 1.2% false alarm rate. We illustrate the model's application in monitoring traffic over a week, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors with visual information, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our dataset.
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