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Power Requirements Evaluation of Embedded Devices for Real-Time Video Line Detection

Author

Listed:
  • Jakub Suder

    (Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, Piotrowo 3A, 60-965 Poznan, Poland)

  • Kacper Podbucki

    (Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, Piotrowo 3A, 60-965 Poznan, Poland)

  • Tomasz Marciniak

    (Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, Piotrowo 3A, 60-965 Poznan, Poland)

Abstract

In this paper, the comparison of the power requirements during real-time processing of video sequences in embedded systems was investigated. During the experimental tests, four modules were tested: Raspberry Pi 4B, NVIDIA Jetson Nano, NVIDIA Jetson Xavier AGX, and NVIDIA Jetson Orin AGX. The processing speed and energy consumption have been checked, depending on input frame size resolution and the particular power mode. Two vision algorithms for detecting lines located in airport areas were tested. The results show that the power modes of the NVIDIA Jetson modules have sufficient computing resources to effectively detect lines based on the camera image, such as Jetson Xavier in mode MAXN or Jetson Orin in mode MAXN, with a resolution of 1920 × 1080 pixels and a power consumption of about 19 W for 24 FPS for both algorithms tested.

Suggested Citation

  • Jakub Suder & Kacper Podbucki & Tomasz Marciniak, 2023. "Power Requirements Evaluation of Embedded Devices for Real-Time Video Line Detection," Energies, MDPI, vol. 16(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6677-:d:1242264
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    References listed on IDEAS

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    1. Dong-Ki Kang & Ki-Beom Lee & Young-Chon Kim, 2022. "Cost Efficient GPU Cluster Management for Training and Inference of Deep Learning," Energies, MDPI, vol. 15(2), pages 1-20, January.
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