Should I prefer cropped images or realistic images for object detection? I am new to the field of AI but due to the high level of abstraction that comes with services such as Google VisionAI I got motivated to write an application that detects symbols in photos based on tensorflow.js and a custom model trained in Google Vision AI.
My App is about identifying symbols in photos, very similar to traffic signs or logo detection. Now I wonder if

I should train the model based on real, distorted and complex photos that contain those symbols and lots of background noise
if it was enough to train the model based on cropped, clean symbols
A hybrid of both

I started with option a and it works fine, however it was a lot of work to create the training dataset. Does the model need the distorted background to work?
Google recommandation seems to answer this:

The training data should be as close as possible to the data on which
predictions are to be made.
For example, if your use case involves blurry and low-resolution
images (such as from a security camera), your training data should be
composed of blurry, low-resolution images. In general, you should also
consider providing multiple angles, resolutions, and backgrounds for
your training images.

https://cloud.google.com/vision/automl/object-detection/docs/prepare
Would you agree, also in the case of symbols?