Counting in a heap of fruits

Deep Learning
1

Hello friends,

I want to build a model which can count the number of coconuts in a heap. I am planning to manually collect data for this. I have found this interesting paper. Few questions I have.

I see that there is no way I can detect coconuts as a heap completely covers many coconuts inside. So I want to consider it as a regression problem.

  1. Which angle of the heap should I collect data? The top view (of the heap) seems to consider the height of the heap as well, but not easy to collect.
  2. Maybe I should start with a smaller fruit like lemon so that I can easily collect data and train and later do transfer learning on coconuts?
  3. A square image of 5 MP should be good enough for collecting? Or is it an overkill?
  4. Any place where I can get pre-trained weights to start with?

Please feel free to express your thoughts.

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2

Surely the CoCo dataset :slight_smile:

Seriously though, assuming you have the luxury of time and a bigger model, perhaps an object detection solution would work well?

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3

In a heap (pyramid kind of), many coconuts are hidden deep inside. There is no way to detect each coconut.

4

I’ve really no idea whether this has any relevance, but maybe it’s worth trying; https://github.com/diptodip/counting

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