A Deep Learning Image Segmentation Model for Detection of Weak Vehicle-Generated Quasi-Static Strain in Distributed Acoustic Sensing
Fei Peng, Ziyang Zhu, Yujie Zhang, Qiang Miao
Abstract
Distributed Acoustic Sensing (DAS) instrument connected to dark fibers that widely deployed near roads can collect quasi-static strain signals generated by vehicles over a large range. This approach addresses the high deployment and maintenance costs and limited coverage of traditional roadside sensing technologies, making DAS a highly promising vehicle detection technology for intelligent transportation systems. However, using existing communication cables rather than specially laid sensing cables as the DAS sensing medium, while offering ultra-low deployment and maintenance cost advantages, poses significant challenges for detecting lightweight, low-speed vehicles that are far from the fiber cable. The quasi-static strain generated by such vehicles are low and easily overwhelmed by environmental noise and DAS fading noise. In this paper, we analyze the causes of weak vehicle quasi-static signals and propose a Unet image segmentation network, trained to recognize these weak signals using a large window with a small step size for data input. In a typical campus test scenario containing numerous lightweight low-speed vehicles, we tested various vehicle quasi-static signals using Unet. The results demonstrated that our method has high recognition accuracy and excellent resistance to DAS fading noise.