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Research on Over-the-Horizon Perception Distance Division of Optical Fiber Communication Based on Intelligent

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This study introduces a cloud-edge architecture and data protocol for smart highways, enhancing real-time vehicle tracking using StreamYOLO and fiber optic networks for improved perception.

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Area of Science:

  • Intelligent Transportation Systems
  • Networked Infrastructure
  • Edge Computing

Background:

  • Intelligent networking projects deploy advanced roadside digital infrastructure for real-time road situation sensing.
  • Data transmission relies on high-speed optical fiber networks connecting roadside devices to edge computing and cloud platforms.

Purpose of the Study:

  • To propose a cloud-edge terminal architecture system with cloud-edge cooperation.
  • To develop a data exchange protocol for cloud control basic platforms.
  • To validate the effectiveness of optical fiber networks in over-the-horizon perception for intelligent roadways.

Main Methods:

  • Deployment of intelligent roadside devices on an intelligent highway.
  • Implementation of a cloud-edge terminal architecture and data exchange protocol.
  • Utilizing optical fiber network communication algorithms and the ModelScope large model for real-time video data inference.
  • Employing the StreamYOLO model with Streaming Perception for target vehicle detection and tracking.

Main Results:

  • The proposed architecture and protocol were verified through deployment on an intelligent highway.
  • The StreamYOLO model successfully detected and continuously tracked vehicles in real-time video data.
  • Experimental validation confirmed the high application value of fiber optic networks in intelligent roadway perception.

Conclusions:

  • The developed cloud-edge system and data protocol are effective for intelligent roadways.
  • Fiber optic networks play a crucial role in enabling advanced perception capabilities in smart highway infrastructure.
  • The StreamYOLO model demonstrates strong performance for real-time vehicle tracking in intelligent transportation systems.