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An Optimized DNN Model for Real-Time Inferencing on an Embedded Device.

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Researchers optimized deep neural network (DNN) models for real-time vehicle detection in Advanced Driver Assist Systems (ADAS). The enhanced DNN models achieve higher accuracy and significantly faster processing speeds for automotive applications.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Automotive Engineering

Background:

  • Deep Neural Networks (DNNs) are crucial for object detection in Advanced Driver Assist Systems (ADAS) and Autonomous Driving (AD).
  • High computational costs of current DNN models hinder real-time deployment in vehicles.
  • Low response times and high accuracy are critical for automotive safety systems.

Purpose of the Study:

  • To deploy computer-vision-based object detection systems for real-time automotive applications.
  • To develop and optimize DNN models for efficient in-vehicle inferencing.
  • To improve the performance of vehicle detection systems for ADAS.

Main Methods:

  • Developed five vehicle detection systems using transfer learning with pre-trained DNN models.
  • Optimized the best-performing DNN model through horizontal and vertical layer fusion.
  • Deployed the optimized DNN model on an embedded in-vehicle computing device (NVIDIA Jetson AGA).

Main Results:

  • The best DNN model improved Precision by 7.1%, Recall by 10.8%, and F1 score by 8.93% compared to YOLOv3.
  • The optimized DNN model achieved a processing speed of 35.082 frames per second (fps).
  • Optimization resulted in a 19.385 times speed increase compared to the unoptimized model.

Conclusions:

  • The optimized transferred DNN model offers a viable solution for real-time vehicle detection in ADAS.
  • Achieved a balance between high accuracy and fast processing times, crucial for automotive safety.
  • Demonstrated the effectiveness of model optimization techniques for deploying DNNs in resource-constrained automotive environments.