论文标题
使用深度学习的分布式声传感器系统，用于智能运输
A Distributed Acoustic Sensor System for Intelligent Transportation using Deep Learning
论文作者
论文摘要
智能运输系统（ITS）对可持续和绿色城市生活的发展至关重要。它是数据驱动的，并通过从气管到智能相机的传感器大量来启用。这项工作探讨了基于基于光纤的分布式声传感器（DAS）的新数据源，以进行交通分析。检测车辆类型和估计车辆的占用是其主要关注点。第一个是由于需要跟踪，控制和预测流量的动机。第二个目标是对高占用车辆车道进行调节，以减少排放和拥塞。这些任务通常是通过检查车辆或使用新兴计算机视觉技术来执行的。前者不可扩展或有效，而后者对乘客的隐私是侵入性的。为此，我们提出了一种深度学习技术，以分析DAS信号，以通过连续感应和不暴露个人信息来应对这一挑战。我们提出了一种处理DAS信号并获得92％车辆分类精度的深度学习方法，并根据在受控条件下收集的DAS数据获得92-97％的占用检测。
Intelligent transport systems (ITS) are pivotal in the development of sustainable and green urban living. ITS is data-driven and enabled by the profusion of sensors ranging from pneumatic tubes to smart cameras. This work explores a novel data source based on optical fibre-based distributed acoustic sensors (DAS) for traffic analysis. Detecting the type of vehicle and estimating the occupancy of vehicles are prime concerns in ITS. The first is motivated by the need for tracking, controlling, and forecasting traffic flow. The second targets the regulation of high occupancy vehicle lanes in an attempt to reduce emissions and congestion. These tasks are often conducted by individuals inspecting vehicles or through the use of emerging computer vision technologies. The former is not scale-able nor efficient whereas the latter is intrusive to passengers' privacy. To this end, we propose a deep learning technique to analyse DAS signals to address this challenge through continuous sensing and without exposing personal information. We propose a deep learning method for processing DAS signals and achieve 92% vehicle classification accuracy and 92-97% in occupancy detection based on DAS data collected under controlled conditions.
