Intelligent Vehicle Automatic Identification and Classification With Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) is an emerging vibration collection technology with advantages such as low cost, high-density sampling, and high sensitivity. It utilizes regional unlit fiber-optic telecommunication infrastructure (dark fiber) in the urban underground to record real-time environmental signals. How to identify vehicle signals, classify vehicle types, and estimate vehicle speeds from DAS signals has significant potential for the development of intelligent urban transportation. Addressing the challenges of high-noise environments and dense traffic, we develop an end-to-end two-stage deep learning process to identify and classify various vehicle signals in urban traffic rapidly. First, we propose the CarDenoiseNet network, based on Generative Adversarial Networks (GAN) and contrastive learning, to denoise and enhance the weak signal of DAS data. Then, the YOLOv8 segmentation model is employed to segment and classify the vehicle signal. Finally, the vehicle speeds are estimated using the segmented vehicle trajectory time and location information. We use the urban DAS field data recorded in the downtown area of Changchun to test the proposed workflow. The test result has good generalization and over 90% accuracy in identifying different vehicle types and speeds in high-density traffic environments. Moreover, transfer learning successfully applies the model to other datasets, proving its excellent generalization ability. Additionally, statistical analysis of traffic flow and speed trends provides technical references for alleviating urban traffic congestion, reducing traffic accidents, and enhancing the intelligence level of urban traffic management.