CODEWITHSUNDEEP
imagemachine-learningimage-processingcomputer-visionobject-detection
F16Falcon
F16Falcon

Reputation: 405

What image characteristics are best for training object detection models?

I'm trying to create a model to detect a few objects in my custom photos. Before training my model, I'd like to know if and how I should modify my images to improve its accuracy.

I don't have access to the photos at the moment, however, I can provide an example of the characteristics of the images I'll be working with:

  1. There's a white piece of paper (so white background), and on it are a bunch of insects.
  2. There are a few different kinds of insects, and they look unique from eachother (different colors, shapes, sizes etc.).
  3. The camera is pretty zoomed out, so each insect is probably ~ 40x40 pixels (so it's not really high definition).

I don't know much about machine learning, but I'd assume that because the insects will be captured in low quality, the model will mainly end up relying on the general shape and color to distinguish/identify the insects (e.g. long or circular spot on photo, etc.).

Therefore, I was wondering if I should do anything to to the photos to achieve higher accuracy (before I train it). For example, if I increase the contrast in my photos, would the insect's borders be more defined and thus make it easier for the model to detect/identify them? Or, should I convert the images to grayscale or stick with RGB? Are there any other factors that should be considered?

Upvotes: 0

Views: 483

Answers (1)

Karan Dhingra
Karan Dhingra

Reputation: 133

You an employ standard preprocessing strategy like

  1. Normalization of the RGB values
  2. Horizontal/Vertical flipping
  3. Affine transformation

Upvotes: 1

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