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Updated: Sep 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Adaptive Real-Time Object Detection for Autonomous Driving Systems.

Maryam Hemmati1, Morteza Biglari-Abhari1, Smail Niar2

  • 1Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland 1010, New Zealand.

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|April 21, 2022
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Summary
This summary is machine-generated.

This study introduces an adaptive system for autonomous driving that detects pedestrians and vehicles in varying light conditions. The dynamically reconfigurable system maintains real-time performance with minimal resource overhead.

Keywords:
Adaptive ADSDBNFPGAHOGSVMhardware acceleratorpartial reconfigurationpedestrian detectionreal-time detectionvehicle detection

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Area of Science:

  • Computer Vision
  • Embedded Systems Engineering
  • Robotics

Background:

  • Autonomous Driving Systems (ADS) require accurate obstacle detection for reliability.
  • Environmental factors like lighting conditions complicate real-time detection.
  • Resource constraints and energy efficiency are critical design challenges for ADS.

Purpose of the Study:

  • To develop an adaptive system for detecting pedestrians and vehicles under diverse lighting conditions.
  • To address the computational intensity and system complexity in ADS.
  • To meet the real-time performance requirements of autonomous driving.

Main Methods:

  • A hardware-software co-design approach was implemented on Zynq UltraScale+ MPSoC.
  • A dynamically reconfigurable system was developed for adaptive detection.
  • Hardware accelerators were utilized for pedestrian and vehicle detection.

Main Results:

  • The system demonstrated adaptability to varying environmental lighting conditions.
  • Real-time performance was maintained throughout the detection process.
  • Minimal resource overhead was achieved with the adaptive approach.

Conclusions:

  • The proposed adaptive system effectively detects pedestrians and vehicles in different lighting conditions.
  • Hardware-software co-design enables dynamic reconfiguration for optimized ADS performance.
  • The system offers a viable solution for reliable and efficient autonomous driving perception.