A Deep Learning Image Segmentation Model for Detection of Weak Vehicle-Generated Quasi-Static Strain in Distributed Acoustic Sensing

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.