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The concept of work involves force and displacement; meanwhile, the work-energy theorem relates the net work done on a body to the difference in its kinetic energy, calculated between two points on its trajectory. While none of these quantities or relations involves time explicitly, we know that the time available to accomplish work is often just as important as the amount of work itself. For example, sprinters in a race may have achieved the same velocity at the finish, therefore,...
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DAC-SDC Low Power Object Detection Challenge for UAV Applications.

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    The 2018 System Design Contest challenged participants to develop low-power object detection algorithms for unmanned aerial vehicles. This competition spurred innovation in efficient image analysis for drone applications.

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    Area of Science:

    • Computer Vision
    • Embedded Systems Design
    • Algorithm Optimization

    Background:

    • The 55th Design Automation Conference (DAC) introduced the System Design Contest (SDC) in 2018.
    • SDC'18 focused on the Low Power Object Detection Challenge (LPODC).
    • The challenge involved designing algorithms for object detection in Unmanned Aerial Vehicle (UAV) imagery.

    Purpose of the Study:

    • To detail the dataset and evaluation procedures for the DAC-SDC'18 LPODC.
    • To present novel algorithms and methods developed by contest participants.
    • To analyze representative results and identify future research directions.

    Main Methods:

    • Utilized a dataset comprising 150,000 images across 95 categories.
    • Employed hardware platforms including Nvidia's TX2 and Xilinx's PYNQ Z1.
    • Evaluated participant-developed object detection algorithms for power efficiency and accuracy.

    Main Results:

    • Over 110 entries from 12 countries participated in the DAC-SDC'18.
    • Demonstrated various algorithmic approaches for low-power object detection on embedded platforms.
    • Achieved competitive results in detecting objects within UAV imagery.

    Conclusions:

    • The contest successfully fostered advancements in efficient object detection for UAVs.
    • Highlighted the potential for novel algorithms in real-world, resource-constrained applications.
    • Identified key areas for future improvements in algorithm design and hardware implementation.