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An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

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Summary
This summary is machine-generated.

This study introduces an edge computing system using Field-Programmable Gate Arrays (FPGAs) for real-time pavement defect detection in Advanced Driver-Assistance Systems (ADAS). The system enhances vehicle safety and performance through accurate defect identification and communication.

Keywords:
FPGAadvanced driver-assistance systemsdeep learning processing unitedge computing system

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Area of Science:

  • Edge Computing
  • Field-Programmable Gate Arrays (FPGAs)
  • Advanced Driver-Assistance Systems (ADAS)

Background:

  • Real-time applications benefit from edge computing and FPGAs.
  • Pavement defect detection is crucial for vehicle safety and performance.

Purpose of the Study:

  • To design an innovative edge computing system for pavement defect detection in ADAS.
  • To integrate AMD Xilinx AI platform and YOLOv3 model for efficient defect identification.

Main Methods:

  • Utilized cameras for road scene capture.
  • Employed a Deep Learning Processing Unit (DPU) with the YOLOv3 model for defect detection.
  • Integrated FPGA technology for customized circuit configuration and real-time processing.

Main Results:

  • Achieved high accuracy and efficiency in identifying three types of pavement defects.
  • Enabled real-time transmission of defect information via Controller Area Network (CAN) interface.
  • Transformed ADAS into an edge computing device for informed decision-making.

Conclusions:

  • FPGA-based edge computing significantly enhances ADAS capabilities for automotive safety.
  • The system facilitates real-time communication and decision-making, improving driving experience.
  • This synergy paves the way for future innovations in vehicle assistance systems.