ML.Net ONNX Object Detection on ARM64 ASUS PE100A

I work on applications which need a device that will survive in a farm shed that is open to all weathers. The ASUS PE100A an ARM64 device which, with the right parts has an operational temperature range of -20~60°C should be fine for New Zealand conditions. The devices are usually shipped with Windows 10 IoT Core or Yocto but an Ubuntu image (which this post uses) is also available.

The Ubuntu install is distributed as a zip file which contains the NXP IMX flashing utility (uuu.exe), installation scripts and the device image. I won’t cover the process in detail as the very helpful local ASUS support person and the readme file were more than sufficient.

Contents of PE100 Ubuntu update
PE100 device dip switches which control boot process(see readme for details)
PE100 flashing process complete

After remembering to reset the DIP switches before powering up the device it booted to a simple console.

PE100 Ubuntu home screen

I then created a new user, set the password, updated the users permissions and manually installed the .Net Core runtime (using a hybrid of the Microsoft and these instructions). I then had a device that I could SSH into, copy files to with WinSCP and run simple console applications on.

I then deployed my ONNX Object Detection console application to the device and it wouldn’t start. I had forgotten to install support for System.Drawing.Common with

sudo apt-get install -y libgdiplus

Object Detection console application with code to draw MBRs on images
Object Detection console application without code to draw MBRs on images

The version of the application which draws Minimum Bounding Boxes(MBRs) on the output images was only slightly slower that the version which didn’t. (the PE100 has a 16G on board eMMC so disk access is going to be fairly quick)

The required operational temperature range and price point make the PE100A good platform for our product.