I need help to detect something specific

So i need to detect a stack of logs from about the range of max 35 meters from every angle in various weather conditions (snow, rain, everything) and every time (night and day). I would have an infrared camera, rgb camera (i would also buy something new if that would help capturing a good image during day and night. The stack of logs can be cut like in the picture but also can full on trees stacked. So yeah my problem is i dont know on wich parts of the tree i should focus on when detecting or training, because somtimes i only see the tree slices and sometimes only the bark from the side and sometimes a mix of both. The detection should be pretty accurate too. So if anyone has an idea i would really appreciate it.

Hey @Hatzi

Thanks for sharing your interesting use case. I imagine this project might take a bit of trial and error, but here’s a couple thoughts that come to mind:

  • Computer vision models perform well when predicting on data they “see” during training.
    • If you have a lot of environmental variability, like weather and time of day, you might have better success with an infrared camera (given its consistently easier to spot the logs w/ an infrared)
  • If there’s a distinctive part of the trees that are always somewhat visible, like the bark, training an object detection model with one class (ex: logpile) might work well for your use case
    • If there are several different distinctive scenarios with minimal visual overlap, (only the tree core/rings, vs only the bark, vs only the leaves) you might have better luck with several classes (ex: logpile-bark, logpile-leaves, logpile-core)
    • I would recommend starting to label with several different classes. This way, you can train a model with these multiple classes, and if it performs poorly you can always remap them to a single class to see if it performs better.

Hope this helps