Hi everyone - I'm working on a vehicle that will steer autonomously in an agricultural setting. I want to use an oak-d-lite to determine where a furrow is (the path between the crops, ie where a tractor drives) in a field and use that information to drive in the center of the furrow. After looking at some existing ML model databases, I think that I'm going to have to develop my own model to do this.
As I understand it, the steps to do this are
- use an oak-d-lite to take thousands of photos of furrows
- plug these photos into a ML framework like tensorflow to create a model
- compile the model and run it on the oak-d-lite
So my questions are -
1) Does all of the above seem correct?
2) Is there a preferred format for using depth and RGB data in a training set? Should I preprocess the data in some way?
3) Is there a preferred software stack for dealing with depth data or one that is especially easy to use for these kinds of use cases? I was going to use tensorflow just because it was the only one I had heard of before I starting this project.
As you can probably tell, I'm brand new to the world of computer vision and AI so any help would be much appreciated. Thanks!
