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Run Your 3D Object Detector on NVIDIA Jetson Platforms:A Benchmark Analysis.

Chungjae Choe1, Minjae Choe2, Sungwook Jung1

  • 1Autonomous IoT Research Center, Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea.

Sensors (Basel, Switzerland)
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Area of Science:

  • Robotics and Artificial Intelligence
  • Embedded Systems and Edge Computing

Background:

  • Autonomous navigation relies on real-time 3D object detection for collision avoidance.
  • NVIDIA Jetson platforms offer onboard GPU computation suitable for robotic edge processing.
  • A comprehensive benchmark for deep learning 3D object detection on Jetson platforms is lacking.

Purpose of the Study:

  • To benchmark NVIDIA Jetson platforms (Nano, TX2, NX, AGX) using deep learning 3D object detection frameworks.
  • To evaluate the performance impact of the TensorRT library on inference speed and resource utilization.
  • To establish research foundations for efficient edge device-based 3D object detection in robotics.

Main Methods:

  • Performance analysis of state-of-the-art 3D object detectors across all commercially available NVIDIA Jetson boards.
  • Evaluation of detection accuracy, frames per second (FPS), and resource usage (GPU, CPU, memory, power).
  • Assessment of the TensorRT library's optimization effects on deep learning models for Jetson platforms.

Main Results:

  • All tested Jetson boards utilized over 80% of GPU resources on average.
  • TensorRT optimization resulted in a fourfold increase in inference speed.
  • TensorRT reduced central processing unit (CPU) and memory consumption by approximately 50%.

Conclusions:

  • NVIDIA Jetson platforms demonstrate potential for real-time 3D object detection in robotics.
  • TensorRT is a critical tool for optimizing deep learning models on Jetson for enhanced performance and efficiency.
  • This benchmark provides essential data for developing efficient, edge-based autonomous navigation systems.