FPGA HW Acceleration Social Distancing Monitor

The world is now under the cloud of Covid-19 and it has been a hard time when the virus is spreading across the world.

Since keeping distance and wearing a mask really help to reduce the risk of infection. So I decide to do something for distance detection, which I know is just a prototype and far from being used in real life. But I believe with the combined efforts of all around the world, we can overcome such frustration.

I have not that much experience in using Xilinx products and FPGA development. This contest really gives me a change to know the power and flexibility of FPGA. And I see some improvements that can be done to make this project really meaningful.

FPGA_HW_Acceleration_Social_Disatancing_Monitor

Social Disatancing Monitor using yolov3 and DPU HW acceleration for Xilinx adaptive computing challenge 2020

Hardware layout：

Image test for functionality：

How to reproduce this project

You should follow the steps below to reproduce it.

• Download PYNQ image v2.5 from https://github.com/Avnet/Ultra96-PYNQ/releases .
• Image the SD card and boot the device, with USB camera attached, USB to Ethernet adapter to connect to Internet and a mini-DP to DP cable connecting to a Monitor.
• Follow the instructions on DPU-PYNQ page to set up the examples(Xilinx/DPU-PYNQ: DPU on PYNQ (github.com)) by getting all the notebooks, go to this page(DPU-PYNQ/README.md at master · Xilinx/DPU-PYNQ (github.com)), and download the.hwh file for Ultra96v2 to employ DPU acceleration.
• Run the dpu_yolo_v3.ipynb example project and make sure there is no warnings or errors when executing this ipynb file.
• Shift to this project, run one by one and you will get the right results.

If there is one python package missing, please install it on your own. (for I almost forget when I install the other used packages that are not installed when installing dpu-pynq)