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This repository was archived by the owner on Nov 16, 2023. It is now read-only.
This repository was archived by the owner on Nov 16, 2023. It is now read-only.

IR normalization with current variable px distributions #32

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@readicculus

Not a collaborator so could not re-open the closed normalization issue but I've been working on this on my own and have spent a lot of time on the IR imagery. The current issue, which I think its not possible to get around, is that there is no standard distribution of values nor can it be expected. This means that it's not too difficult to do percentile normalization on the given dataset but when it comes to images without hotspots the normalization causes warmer areas to look like hotspots.

There are a lot of pools of water that look very similar to seals in shape and size but are not. Without some sort of temperature to pixel correlation I've not found a way to do this so that it won't detect a ton of FPs on images without seals or other animals.

It sounds like in future flights the camera will include a temp correlation to at least the max pixel value but until then I'm thinking that the IR data is unfortunately relatively useless. IR imagery is suuuper important as I was getting an F1 score of around .93 on the labeled images and its WAYYYY faster to localize on them than the RGB imagery. Ideally we need to achieve about 3fps localization to be able to run this live on the plane which is seeming impossible with the large rgb images where it's taking me about 1-4 secs per image depending on the model and chip size.

Any feedback would be great as getting IR to work would be a massive step forward in this project.

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