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ConcentrateNet: Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant

Bo-Xun Wu1, Vinay M Shivanna1, Hsiang-Hsuan Hung2

  • 1Department of Electronics Engineering, Institute of Electronics, National Yang Ming Chiao Tung University (NYCU), Hsinchu 30010, Taiwan.

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Summary

This study introduces ConcentrateNet, a deep learning model for real-time object detection in Advanced Driver Assistance Systems (ADAS). ConcentrateNet effectively detects distant objects by focusing on vanishing points, improving safety for autonomous machines.

Keywords:
Advanced Driver Assistance Systems (ADAS)deep learningembedded systemneural networksobject detection

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Autonomous Systems

Background:

  • Advanced Driver Assistance Systems (ADAS) face challenges in detecting small, distant objects.
  • Current methods struggle to maintain detection confidence for objects at varying distances.

Purpose of the Study:

  • To propose an efficient deep learning object detection network, ConcentrateNet, for real-time detection of distant objects.
  • To improve the detection of small and faraway objects in vehicular perspectives.

Main Methods:

  • ConcentrateNet utilizes a two-stage inference process: initial detection with a large receptive field to predict a vanishing point, followed by cropped image processing near the vanishing point.
  • The network architecture is designed for multi-scale object detection and integrates with existing state-of-the-art models.
  • A specialized Non-Maximum Suppression (NMS) method merges results from the two inference stages.

Main Results:

  • ConcentrateNet achieves significant precision and recall improvements compared to traditional high-resolution input models.
  • The proposed network uses lower input resolution and has less model complexity.
  • The model was successfully deployed on a low-powered embedded system (NVIDIA Jetson AGX Xavier) for real-time performance.

Conclusions:

  • ConcentrateNet offers an efficient and effective solution for real-time distant object detection in ADAS applications.
  • The architecture's adaptability and performance on embedded systems make it suitable for autonomous machines.
  • This approach enhances the capability of detecting critical faraway objects, contributing to safer autonomous driving.