Divide classes into truncated and non-truncated objects At the moment I am working on a vehicle counting &amp; classification project. 
For a specific part in the project I need to get back only the completely visible vehicles from my input data (images).  I am wondering if this could be done (more) automatically in the following way:


zoom in such that only approximately one van would be visible
divide the vehicles into two categories: truncated and non-truncated
train on these two classes
After training and testing, use the model to find the completely visible vehicles. 


So the main question is, is it possible that this would give sufficient results or should I try to find another solution?
It could work. I think itll be hard to find labelled bounding box data describing full/truncated cars so this is a good idea for self labelling.

Theres probably also some other ways for you to get labelled data without the crowd sourcing or self labelling. Take a large labelled car dataset with bounding boxes (COCO has some, and if your good with aerial imagery theres alot more out there), and just enforce that any car bounding boxes that are touching the boundary are truncated, while ones fully encompassed in the imaged are not, since with high probability that will be the case. 

Good luck!