基于对抗生成网络的分布式光纤声波车辆传感信号高性能去噪方法

High-Performance Denoising Method for Distributed Fiber Optic Acoustic Vehicle Sensing Signals Based on Generative Adversarial Networks

摘要:

路侧车辆检测系统为智能交通系统提供了关键的数据支持。目前,传统的车辆检测技术仍面临高维护和部署成本等问题。基于分布式光纤声波传感系统(DAS)已经被证明能够提供被动、广范围且高空间分辨率的车辆检测与定位,且无需现场安装传感器设备,因此具有超低部署成本的优势。然而,基于DAS的车辆检测系统容易受到环境噪声和系统衰落影响。为解决该问题,本文提出了一种多尺度上下文对抗生成式网络(MCGAN),通过多尺度空洞卷积提取特征,整合不同尺度的层次化信息,提升模型去噪性能。实验结果表明,MCGAN能抑制环境噪声和衰落噪声,同时较为完整保留车辆信号,尤其在低速小型车辆信号中表现优异。

Abstract:

Roadside vehicle detection system provides critical data support for intelligent transportation systems. However, traditional vehicle detection technologies still face challenges such as high maintenance and deployment costs. The Distributed Acoustic Sensing (DAS) system has been shown to offer passive, wide-range, and high spatial resolution vehicle detection and localization, without the need for on-site sensor installation, thus providing a significant advantage in terms of ultra-low deployment costs. However, DAS-based vehicle detection systems are susceptible to environmental noise and system fading. To address this issue, this paper proposes a Multi-Scale Context Generative Adversarial Network (MCGAN), which extracts features using multi-scale dilated convolutions and integrates hierarchical information across different scales to enhance the model''s denoising performance. Experimental results demonstrate that MCGAN effectively suppresses environmental and fading noise while preserving vehicle signals, particularly excelling in detecting low-speed and small vehicle signals.