YOLOv5 on FPGA with Hailo-8 and 4 Pi Cameras

Re:YOLOv5 on FPGA with Hailo-8 and 4 Pi Cameras by dekhn on Friday May 31, 2024 @11:07AM (#40536154)

I use object detection to track tardigrades in my custom motorized microscope. It's very useful for making long observations in a field much larger than the scope's field of view.

The system works quite simply: I start with an existing object detector and train it with a small (<100) number of manually labelled images. Then during inference, I move the scope's field of view using motor commands to put the center of the tardigrade at the center of the field of view.

This technology is very useful for doing long-term observations of tardigrades (so, useful for science).

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Re:YOLOv5 on FPGA with Hailo-8 and 4 Pi Cameras by eurekin on Friday May 31, 2024 @11:36AM (#40536520)

Thank you! Is the detection accurate enough, or simply the observation conclusions are not that sensitive to minor errors?

That makes me want to revisit my previous idea: boiling soup spillage detector. I once had a google meeting with a cooking soup to keep an eye on it and thought, heck, that seems like a nice exercise for a visual detector finetune

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Re:YOLOv5 on FPGA with Hailo-8 and 4 Pi Cameras by dekhn on Friday May 31, 2024 @11:46AM (#40536632)

The detection was accurate enough for me to complete one prototype experiment under controlled conditions- a single tardigrade in an otherwise empty field, and even then, it did lose the tardigrade once or twice. Different lighting conditions, and other things in the field like tardigrade eggs, algae and dirt all make it more challenging.

To make it truly ready for production science, I'd need to put more work into making the model robust. I'd also like better object tracking, so I could track multiple unique tardigrades.

If you want to see even better examples, take a look at DeepLabCut, https://www.mackenziemathislab.org/deeplabcut especially the video examples.

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